CINXE.COM

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Search results for: Kalman filtering</title> <meta name="description" content="Search results for: Kalman filtering"> <meta name="keywords" content="Kalman filtering"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" type="button" data-toggle="collapse" data-target="#navbarMenu" aria-controls="navbarMenu" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="w-100"> <div class="d-none d-lg-flex flex-row-reverse"> <form method="get" action="https://waset.org/search" class="form-inline my-2 my-lg-0"> <input class="form-control mr-sm-2" type="search" placeholder="Search Conferences" value="Kalman filtering" name="q" aria-label="Search"> <button class="btn btn-light my-2 my-sm-0" type="submit"><i class="fas fa-search"></i></button> </form> </div> <div class="collapse navbar-collapse mt-1" id="navbarMenu"> <ul class="navbar-nav ml-auto align-items-center" id="mainNavMenu"> <li class="nav-item"> <a class="nav-link" href="https://waset.org/conferences" title="Conferences in 2024/2025/2026">Conferences</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/disciplines" title="Disciplines">Disciplines</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/committees" rel="nofollow">Committees</a> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" id="navbarDropdownPublications" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> Publications </a> <div class="dropdown-menu" aria-labelledby="navbarDropdownPublications"> <a class="dropdown-item" href="https://publications.waset.org/abstracts">Abstracts</a> <a class="dropdown-item" href="https://publications.waset.org">Periodicals</a> <a class="dropdown-item" href="https://publications.waset.org/archive">Archive</a> </div> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/page/support" title="Support">Support</a> </li> </ul> </div> </div> </nav> </div> </header> <main> <div class="container mt-4"> <div class="row"> <div class="col-md-9 mx-auto"> <form method="get" action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Kalman filtering"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 440</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Kalman filtering</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">440</span> A Filtering Algorithm for a Nonlinear State-Space Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdullah%20Eqal%20Al%20Mazrooei">Abdullah Eqal Al Mazrooei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Kalman filter is a famous algorithm that utilizes to estimate the state in the linear systems. It has numerous applications in technology and science. Since of the most of applications in real life can be described by nonlinear systems. So, Kalman filter does not work with the nonlinear systems because it is suitable to linear systems only. In this work, a nonlinear filtering algorithm is presented which is suitable to use with the special kinds of nonlinear systems. This filter generalizes the Kalman filter. This means that this filter also can be used for the linear systems. Our algorithm depends on a special linearization of the second degree. We introduced the nonlinear algorithm with a bilinear state-space model. A simulation example is presented to illustrate the efficiency of the algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title="Kalman filter">Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=filtering%20algorithm" title=" filtering algorithm"> filtering algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20systems" title=" nonlinear systems"> nonlinear systems</a>, <a href="https://publications.waset.org/abstracts/search?q=state-space%20model" title=" state-space model"> state-space model</a> </p> <a href="https://publications.waset.org/abstracts/74331/a-filtering-algorithm-for-a-nonlinear-state-space-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74331.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">375</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">439</span> The Evaluation of the Performance of Different Filtering Approaches in Tracking Problem and the Effect of Noise Variance </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Javad%20Mollakazemi">Mohammad Javad Mollakazemi</a>, <a href="https://publications.waset.org/abstracts/search?q=Farhad%20Asadi"> Farhad Asadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Aref%20Ghafouri"> Aref Ghafouri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Performance of different filtering approaches depends on modeling of dynamical system and algorithm structure. For modeling and smoothing the data the evaluation of posterior distribution in different filtering approach should be chosen carefully. In this paper different filtering approaches like filter KALMAN, EKF, UKF, EKS and smoother RTS is simulated in some trajectory tracking of path and accuracy and limitation of these approaches are explained. Then probability of model with different filters is compered and finally the effect of the noise variance to estimation is described with simulations results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20approximation" title="Gaussian approximation">Gaussian approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20smoother" title=" Kalman smoother"> Kalman smoother</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20estimation" title=" parameter estimation"> parameter estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=noise%20variance" title=" noise variance"> noise variance</a> </p> <a href="https://publications.waset.org/abstracts/14553/the-evaluation-of-the-performance-of-different-filtering-approaches-in-tracking-problem-and-the-effect-of-noise-variance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14553.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">439</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">438</span> Autonomous Position Control of an Unmanned Aerial Vehicle Based on Accelerometer Response for Indoor Navigation Using Kalman Filtering </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Syed%20Misbahuddin">Syed Misbahuddin</a>, <a href="https://publications.waset.org/abstracts/search?q=Sagufta%20Kapadia"> Sagufta Kapadia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Autonomous indoor drone navigation has been posed with various challenges, including the inability to use a Global Positioning System (GPS). As of now, Unmanned Aerial Vehicles (UAVs) either rely on 3D mapping systems or utilize external camera arrays to track the UAV in an enclosed environment. The objective of this paper is to develop an algorithm that utilizes Kalman Filtering to reduce noise, allowing the UAV to be navigated indoors using only the flight controller and an onboard companion computer. In this paper, open-source libraries are used to control the UAV, which will only use the onboard accelerometer on the flight controller to estimate the position through double integration. One of the advantages of such a system is that it allows for low-cost and lightweight UAVs to autonomously navigate indoors without advanced mapping of the environment or the use of expensive high-precision-localization sensors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accelerometer" title="accelerometer">accelerometer</a>, <a href="https://publications.waset.org/abstracts/search?q=indoor-navigation" title=" indoor-navigation"> indoor-navigation</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman-filtering" title=" Kalman-filtering"> Kalman-filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=position-control" title=" position-control "> position-control </a> </p> <a href="https://publications.waset.org/abstracts/115917/autonomous-position-control-of-an-unmanned-aerial-vehicle-based-on-accelerometer-response-for-indoor-navigation-using-kalman-filtering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115917.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">349</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">437</span> Tracking Filtering Algorithm Based on ConvLSTM</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ailing%20Yang">Ailing Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Penghan%20Song"> Penghan Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Aihua%20Cai"> Aihua Cai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The nonlinear maneuvering target tracking problem is mainly a state estimation problem when the target motion model is uncertain. Traditional solutions include Kalman filtering based on Bayesian filtering framework and extended Kalman filtering. However, these methods need prior knowledge such as kinematics model and state system distribution, and their performance is poor in state estimation of nonprior complex dynamic systems. Therefore, in view of the problems existing in traditional algorithms, a convolution LSTM target state estimation (SAConvLSTM-SE) algorithm based on Self-Attention memory (SAM) is proposed to learn the historical motion state of the target and the error distribution information measured at the current time. The measured track point data of airborne radar are processed into data sets. After supervised training, the data-driven deep neural network based on SAConvLSTM can directly obtain the target state at the next moment. Through experiments on two different maneuvering targets, we find that the network has stronger robustness and better tracking accuracy than the existing tracking methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=maneuvering%20target" title="maneuvering target">maneuvering target</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20estimation" title=" state estimation"> state estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=self-attention" title=" self-attention"> self-attention</a> </p> <a href="https://publications.waset.org/abstracts/164893/tracking-filtering-algorithm-based-on-convlstm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164893.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">176</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">436</span> Kalman Filter Gain Elimination in Linear Estimation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nicholas%20D.%20Assimakis">Nicholas D. Assimakis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In linear estimation, the traditional Kalman filter uses the Kalman filter gain in order to produce estimation and prediction of the n-dimensional state vector using the m-dimensional measurement vector. The computation of the Kalman filter gain requires the inversion of an m x m matrix in every iteration. In this paper, a variation of the Kalman filter eliminating the Kalman filter gain is proposed. In the time varying case, the elimination of the Kalman filter gain requires the inversion of an n x n matrix and the inversion of an m x m matrix in every iteration. In the time invariant case, the elimination of the Kalman filter gain requires the inversion of an n x n matrix in every iteration. The proposed Kalman filter gain elimination algorithm may be faster than the conventional Kalman filter, depending on the model dimensions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discrete%20time" title="discrete time">discrete time</a>, <a href="https://publications.waset.org/abstracts/search?q=estimation" title=" estimation"> estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter%20gain" title=" Kalman filter gain"> Kalman filter gain</a> </p> <a href="https://publications.waset.org/abstracts/123040/kalman-filter-gain-elimination-in-linear-estimation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123040.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">195</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">435</span> Stochastic Default Risk Estimation Evidence from the South African Financial Market</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mesias%20Alfeus">Mesias Alfeus</a>, <a href="https://publications.waset.org/abstracts/search?q=Kirsty%20Fitzhenry"> Kirsty Fitzhenry</a>, <a href="https://publications.waset.org/abstracts/search?q=Alessia%20Lederer"> Alessia Lederer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present paper provides empirical studies to estimate defaultable bonds in the South African financial market. The main goal is to estimate the unobservable factors affecting bond yields for South African major banks. The maximum likelihood approach is adopted for the estimation methodology. Extended Kalman filtering techniques are employed in order to tackle the situation that the factors cannot be observed directly. Multi-dimensional Cox-Ingersoll-Ross (CIR)-type factor models are considered. Results show that default risk increased sharply in the South African financial market during COVID-19 and the CIR model with jumps exhibits a better performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=default%20intensity" title="default intensity">default intensity</a>, <a href="https://publications.waset.org/abstracts/search?q=unobservable%20state%20variables" title=" unobservable state variables"> unobservable state variables</a>, <a href="https://publications.waset.org/abstracts/search?q=CIR" title=" CIR"> CIR</a>, <a href="https://publications.waset.org/abstracts/search?q=%CE%B1-CIR" title=" α-CIR"> α-CIR</a>, <a href="https://publications.waset.org/abstracts/search?q=extended%20kalman%20filtering" title=" extended kalman filtering"> extended kalman filtering</a> </p> <a href="https://publications.waset.org/abstracts/151890/stochastic-default-risk-estimation-evidence-from-the-south-african-financial-market" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151890.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">111</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">434</span> Performance Evaluation of GPS/INS Main Integration Approach </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Othman%20Maklouf">Othman Maklouf</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Adwaib"> Ahmed Adwaib </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces a comparative study between the main GPS/INS coupling schemes, this will include the loosely coupled and tightly coupled configurations, several types of situations and operational conditions, in which the data fusion process is done using Kalman filtering. This will include the importance of sensors calibration as well as the alignment of the strap down inertial navigation system. The limitations of the inertial navigation systems are investigated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GPS" title="GPS">GPS</a>, <a href="https://publications.waset.org/abstracts/search?q=INS" title=" INS"> INS</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20calibration" title=" sensor calibration"> sensor calibration</a>, <a href="https://publications.waset.org/abstracts/search?q=navigation%20system" title=" navigation system"> navigation system</a> </p> <a href="https://publications.waset.org/abstracts/1700/performance-evaluation-of-gpsins-main-integration-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1700.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">590</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">433</span> Ultra-Tightly Coupled GNSS/INS Based on High Degree Cubature Kalman Filtering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamza%20Benzerrouk">Hamza Benzerrouk</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Nebylov"> Alexander Nebylov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In classical GNSS/INS integration designs, the loosely coupled approach uses the GNSS derived position and the velocity as the measurements vector. This design is suboptimal from the standpoint of preventing GNSSoutliers/outages. The tightly coupled GPS/INS navigation filter mixes the GNSS pseudo range and inertial measurements and obtains the vehicle navigation state as the final navigation solution. The ultra‐tightly coupled GNSS/INS design combines the I (inphase) and Q(quadrature) accumulator outputs in the GNSS receiver signal tracking loops and the INS navigation filter function intoa single Kalman filter variant (EKF, UKF, SPKF, CKF and HCKF). As mentioned, EKF and UKF are the most used nonlinear filters in the literature and are well adapted to inertial navigation state estimation when integrated with GNSS signal outputs. In this paper, it is proposed to move a step forward with more accurate filters and modern approaches called Cubature and High Degree cubature Kalman Filtering methods, on the basis of previous results solving the state estimation based on INS/GNSS integration, Cubature Kalman Filter (CKF) and High Degree Cubature Kalman Filter with (HCKF) are the references for the recent developed generalized Cubature rule based Kalman Filter (GCKF). High degree cubature rules are the kernel of the new solution for more accurate estimation with less computational complexity compared with the Gauss-Hermite Quadrature (GHQKF). Gauss-Hermite Kalman Filter GHKF which is not selected in this work because of its limited real-time implementation in high-dimensional state-spaces. In ultra tightly or a deeply coupled GNSS/INS system is dynamics EKF is used with transition matrix factorization together with GNSS block processing which is well described in the paper and assumes available the intermediary frequency IF by using a correlator samples with a rate of 500 Hz in the presented approach. GNSS (GPS+GLONASS) measurements are assumed available and modern SPKF with Cubature Kalman Filter (CKF) are compared with new versions of CKF called high order CKF based on Spherical-radial cubature rules developed at the fifth order in this work. Estimation accuracy of the high degree CKF is supposed to be comparative to GHKF, results of state estimation are then observed and discussed for different initialization parameters. Results show more accurate navigation state estimation and more robust GNSS receiver when Ultra Tightly Coupled approach applied based on High Degree Cubature Kalman Filter. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GNSS" title="GNSS">GNSS</a>, <a href="https://publications.waset.org/abstracts/search?q=INS" title=" INS"> INS</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filtering" title=" Kalman filtering"> Kalman filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=ultra%20tight%20integration" title=" ultra tight integration"> ultra tight integration</a> </p> <a href="https://publications.waset.org/abstracts/52009/ultra-tightly-coupled-gnssins-based-on-high-degree-cubature-kalman-filtering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52009.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">280</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">432</span> Switched System Diagnosis Based on Intelligent State Filtering with Unknown Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nada%20Slimane">Nada Slimane</a>, <a href="https://publications.waset.org/abstracts/search?q=Foued%20Theljani"> Foued Theljani</a>, <a href="https://publications.waset.org/abstracts/search?q=Faouzi%20Bouani"> Faouzi Bouani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper addresses the problem of fault diagnosis for systems operating in several modes (normal or faulty) based on states assessment. We use, for this purpose, a methodology consisting of three main processes: 1) sequential data clustering, 2) linear model regression and 3) state filtering. Typically, Kalman Filter (KF) is an algorithm that provides estimation of unknown states using a sequence of I/O measurements. Inevitably, although it is an efficient technique for state estimation, it presents two main weaknesses. First, it merely predicts states without being able to isolate/classify them according to their different operating modes, whether normal or faulty modes. To deal with this dilemma, the KF is endowed with an extra clustering step based fully on sequential version of the k-means algorithm. Second, to provide state estimation, KF requires state space models, which can be unknown. A linear regularized regression is used to identify the required models. To prove its effectiveness, the proposed approach is assessed on a simulated benchmark. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnosis" title=" diagnosis"> diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20Filtering" title=" Kalman Filtering"> Kalman Filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means" title=" k-means"> k-means</a>, <a href="https://publications.waset.org/abstracts/search?q=regularized%20regression" title=" regularized regression"> regularized regression</a> </p> <a href="https://publications.waset.org/abstracts/104370/switched-system-diagnosis-based-on-intelligent-state-filtering-with-unknown-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104370.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">182</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">431</span> A Robust and Adaptive Unscented Kalman Filter for the Air Fine Alignment of the Strapdown Inertial Navigation System/GPS </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jian%20Shi">Jian Shi</a>, <a href="https://publications.waset.org/abstracts/search?q=Baoguo%20Yu"> Baoguo Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Haonan%20Jia"> Haonan Jia</a>, <a href="https://publications.waset.org/abstracts/search?q=Meng%20Liu"> Meng Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Ping%20Huang"> Ping Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Adapting to the flexibility of war, a large number of guided weapons launch from aircraft. Therefore, the inertial navigation system loaded in the weapon needs to undergo an alignment process in the air. This article proposes the following methods to the problem of inaccurate modeling of the system under large misalignment angles, the accuracy reduction of filtering caused by outliers, and the noise changes in GPS signals: first, considering the large misalignment errors of Strapdown Inertial Navigation System (SINS)/GPS, a more accurate model is made rather than to make a small-angle approximation, and the Unscented Kalman Filter (UKF) algorithms are used to estimate the state; then, taking into account the impact of GPS noise changes on the fine alignment algorithm, the innovation adaptive filtering algorithm is introduced to estimate the GPS’s noise in real-time; at the same time, in order to improve the anti-interference ability of the air fine alignment algorithm, a robust filtering algorithm based on outlier detection is combined with the air fine alignment algorithm to improve the robustness of the algorithm. The algorithm can improve the alignment accuracy and robustness under interference conditions, which is verified by simulation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=air%20alignment" title="air alignment">air alignment</a>, <a href="https://publications.waset.org/abstracts/search?q=fine%20alignment" title=" fine alignment"> fine alignment</a>, <a href="https://publications.waset.org/abstracts/search?q=inertial%20navigation%20system" title=" inertial navigation system"> inertial navigation system</a>, <a href="https://publications.waset.org/abstracts/search?q=integrated%20navigation%20system" title=" integrated navigation system"> integrated navigation system</a>, <a href="https://publications.waset.org/abstracts/search?q=UKF" title=" UKF"> UKF</a> </p> <a href="https://publications.waset.org/abstracts/128940/a-robust-and-adaptive-unscented-kalman-filter-for-the-air-fine-alignment-of-the-strapdown-inertial-navigation-systemgps" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128940.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">166</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">430</span> Real-Time Radar Tracking Based on Nonlinear Kalman Filter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Milca%20F.%20Coelho">Milca F. Coelho</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Bousson"> K. Bousson</a>, <a href="https://publications.waset.org/abstracts/search?q=Kawser%20Ahmed"> Kawser Ahmed </a> </p> <p class="card-text"><strong>Abstract:</strong></p> To accurately track an aerospace vehicle in a time-critical situation and in a highly nonlinear environment, is one of the strongest interests within the aerospace community. The tracking is achieved by estimating accurately the state of a moving target, which is composed of a set of variables that can provide a complete status of the system at a given time. One of the main ingredients for a good estimation performance is the use of efficient estimation algorithms. A well-known framework is the Kalman filtering methods, designed for prediction and estimation problems. The success of the Kalman Filter (KF) in engineering applications is mostly due to the Extended Kalman Filter (EKF), which is based on local linearization. Besides its popularity, the EKF presents several limitations. To address these limitations and as a possible solution to tracking problems, this paper proposes the use of the Ensemble Kalman Filter (EnKF). Although the EnKF is being extensively used in the context of weather forecasting and it is being recognized for producing accurate and computationally effective estimation on systems with a very high dimension, it is almost unknown by the tracking community. The EnKF was initially proposed as an attempt to improve the error covariance calculation, which on the classic Kalman Filter is difficult to implement. Also, in the EnKF method the prediction and analysis error covariances have ensemble representations. These ensembles have sizes which limit the number of degrees of freedom, in a way that the filter error covariance calculations are a lot more practical for modest ensemble sizes. In this paper, a realistic simulation of a radar tracking was performed, where the EnKF was applied and compared with the Extended Kalman Filter. The results suggested that the EnKF is a promising tool for tracking applications, offering more advantages in terms of performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title="Kalman filter">Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20state%20estimation" title=" nonlinear state estimation"> nonlinear state estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20tracking" title=" optimal tracking"> optimal tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20environment" title=" stochastic environment"> stochastic environment</a> </p> <a href="https://publications.waset.org/abstracts/107223/real-time-radar-tracking-based-on-nonlinear-kalman-filter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107223.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">146</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">429</span> Fast Accurate Detection of Frequency Jumps Using Kalman Filter with Non Linear Improvements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20E.%20Mohamed">Mahmoud E. Mohamed</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20F.%20Shalash"> Ahmed F. Shalash</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanan%20A.%20Kamal"> Hanan A. Kamal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In communication systems, frequency jump is a serious problem caused by the oscillators used. Kalman filters are used to detect that jump, Despite the tradeoff between the noise level and the speed of the detection. In this paper, An improvement is introduced in the Kalman filter, Through a nonlinear change in the bandwidth of the filter. Simulation results show a considerable improvement in the filter speed with a very low noise level. Additionally, The effect on the response to false alarms is also presented and false alarm rate show improvement. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title="Kalman filter">Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=innovation" title=" innovation"> innovation</a>, <a href="https://publications.waset.org/abstracts/search?q=false%20detection" title=" false detection"> false detection</a>, <a href="https://publications.waset.org/abstracts/search?q=improvement" title=" improvement "> improvement </a> </p> <a href="https://publications.waset.org/abstracts/7978/fast-accurate-detection-of-frequency-jumps-using-kalman-filter-with-non-linear-improvements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7978.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">602</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">428</span> Additive White Gaussian Noise Filtering from ECG by Wiener Filter and Median Filter: A Comparative Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Javidnia">Hossein Javidnia</a>, <a href="https://publications.waset.org/abstracts/search?q=Salehe%20Taheri"> Salehe Taheri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Electrocardiogram (ECG) is the recording of the heart’s electrical potential versus time. ECG signals are often contaminated with noise such as baseline wander and muscle noise. As these signals have been widely used in clinical studies to detect heart diseases, it is essential to filter these noises. In this paper we compare performance of Wiener Filtering and Median Filtering methods to filter Additive White Gaussian (AWG) noise with the determined signal to noise ratio (SNR) ranging from 3 to 5 dB applied to long-term ECG recordings samples. Root mean square error (RMSE) and coefficient of determination (R2) between the filtered ECG and original ECG was used as the filter performance indicator. Experimental results show that Wiener filter has better noise filtering performance than Median filter. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ECG%20noise%20filtering" title="ECG noise filtering">ECG noise filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=Wiener%20filtering" title=" Wiener filtering"> Wiener filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=median%20filtering" title=" median filtering"> median filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20noise" title=" Gaussian noise"> Gaussian noise</a>, <a href="https://publications.waset.org/abstracts/search?q=filtering%20performance" title=" filtering performance"> filtering performance</a> </p> <a href="https://publications.waset.org/abstracts/9623/additive-white-gaussian-noise-filtering-from-ecg-by-wiener-filter-and-median-filter-a-comparative-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9623.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">529</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">427</span> Fault Detection and Isolation in Attitude Control Subsystem of Spacecraft Formation Flying Using Extended Kalman Filters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Ghasemi">S. Ghasemi</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Khorasani"> K. Khorasani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the problem of fault detection and isolation in the attitude control subsystem of spacecraft formation flying is considered. In order to design the fault detection method, an extended Kalman filter is utilized which is a nonlinear stochastic state estimation method. Three fault detection architectures, namely, centralized, decentralized, and semi-decentralized are designed based on the extended Kalman filters. Moreover, the residual generation and threshold selection techniques are proposed for these architectures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=component" title="component">component</a>, <a href="https://publications.waset.org/abstracts/search?q=formation%20flight%20of%20satellites" title=" formation flight of satellites"> formation flight of satellites</a>, <a href="https://publications.waset.org/abstracts/search?q=extended%20Kalman%20filter" title=" extended Kalman filter"> extended Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20detection%20and%20isolation" title=" fault detection and isolation"> fault detection and isolation</a>, <a href="https://publications.waset.org/abstracts/search?q=actuator%20fault" title=" actuator fault"> actuator fault</a> </p> <a href="https://publications.waset.org/abstracts/26418/fault-detection-and-isolation-in-attitude-control-subsystem-of-spacecraft-formation-flying-using-extended-kalman-filters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26418.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">434</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">426</span> Kalman Filter for Bilinear Systems with Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdullah%20E.%20Al-Mazrooei">Abdullah E. Al-Mazrooei </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a new kind of the bilinear systems in the form of state space model. The evolution of this system depends on the product of state vector by its self. The well known Lotak Volterra and Lorenz models are special cases of this new model. We also present here a generalization of Kalman filter which is suitable to work with the new bilinear model. An application to real measurements is introduced to illustrate the efficiency of the proposed algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bilinear%20systems" title="bilinear systems">bilinear systems</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20space%20model" title=" state space model"> state space model</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=application" title=" application"> application</a>, <a href="https://publications.waset.org/abstracts/search?q=models" title=" models"> models</a> </p> <a href="https://publications.waset.org/abstracts/3737/kalman-filter-for-bilinear-systems-with-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3737.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">440</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">425</span> The Unscented Kalman Filter Implementation for the Sensorless Speed Control of a Permanent Magnet Synchronous Motor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Justas%20Dilys">Justas Dilys</a> </p> <p class="card-text"><strong>Abstract:</strong></p> ThispaperaddressestheimplementationandoptimizationofanUnscentedKalmanFilter(UKF) for the Permanent Magnet Synchronous Motor (PMSM) sensorless control using an ARM Cortex- M3 microcontroller. A various optimization levels based on arithmetic calculation reduction was implemented in ARM Cortex-M3 microcontroller. The execution time of UKF estimator was up to 90µs without loss of accuracy. Moreover, simulation studies on the Unscented Kalman filters are carried out using Matlab to explore the usability of the UKF in a sensorless PMSMdrive. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=unscented%20kalman%20filter" title="unscented kalman filter">unscented kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=ARM" title=" ARM"> ARM</a>, <a href="https://publications.waset.org/abstracts/search?q=PMSM" title=" PMSM"> PMSM</a>, <a href="https://publications.waset.org/abstracts/search?q=implementation" title=" implementation"> implementation</a> </p> <a href="https://publications.waset.org/abstracts/143271/the-unscented-kalman-filter-implementation-for-the-sensorless-speed-control-of-a-permanent-magnet-synchronous-motor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143271.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">167</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">424</span> Channel Estimation for LTE Downlink</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rashi%20Jain">Rashi Jain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The LTE systems employ Orthogonal Frequency Division Multiplexing (OFDM) as the multiple access technology for the Downlink channels. For enhanced performance, accurate channel estimation is required. Various algorithms such as Least Squares (LS), Minimum Mean Square Error (MMSE) and Recursive Least Squares (RLS) can be employed for the purpose. The paper proposes channel estimation algorithm based on Kalman Filter for LTE-Downlink system. Using the frequency domain pilots, the initial channel response is obtained using the LS criterion. Then Kalman Filter is employed to track the channel variations in time-domain. To suppress the noise within a symbol, threshold processing is employed. The paper draws comparison between the LS, MMSE, RLS and Kalman filter for channel estimation. The parameters for evaluation are Bit Error Rate (BER), Mean Square Error (MSE) and run-time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=LTE" title="LTE">LTE</a>, <a href="https://publications.waset.org/abstracts/search?q=channel%20estimation" title=" channel estimation"> channel estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=OFDM" title=" OFDM"> OFDM</a>, <a href="https://publications.waset.org/abstracts/search?q=RLS" title=" RLS"> RLS</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=threshold" title=" threshold"> threshold</a> </p> <a href="https://publications.waset.org/abstracts/9169/channel-estimation-for-lte-downlink" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9169.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">355</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">423</span> Statically Fused Unbiased Converted Measurements Kalman Filter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhengkun%20Guo">Zhengkun Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=Yanbin%20Li"> Yanbin Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Wenqing%20Wang"> Wenqing Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Bo%20Zou"> Bo Zou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The statically fused converted position and doppler measurements Kalman filter (SF-CMKF) with additive debiased measurement conversion has been previously presented to combine the resulting states of converted position measurements Kalman filter (CPMKF) and converted doppler measurement Kalman filter (CDMKF) to yield the final state estimates under minimum mean squared error (MMSE) criterion. However, the exact compensation for the bias in the polar-to-cartesian and spherical-to-cartesian conversion are multiplicative and depend on the statistics of the cosine of the angle measurement errors. As a result, the consistency and performance of the SF-CMKF may be suboptimal in large-angle error situations. In this paper, the multiplicative unbiased position and Doppler measurement conversion for 2D (polar-to-cartesian) tracking are derived, and the SF-CMKF is improved to use those conversions. Monte Carlo simulations are presented to demonstrate the statistical consistency of the multiplicative unbiased conversion and the superior performance of the modified SF-CMKF (SF-UCMKF). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=measurement%20conversion" title="measurement conversion">measurement conversion</a>, <a href="https://publications.waset.org/abstracts/search?q=Doppler" title=" Doppler"> Doppler</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=estimation" title=" estimation"> estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=tracking" title=" tracking"> tracking</a> </p> <a href="https://publications.waset.org/abstracts/136726/statically-fused-unbiased-converted-measurements-kalman-filter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136726.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">208</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">422</span> Extended Kalman Filter Based Direct Torque Control of Permanent Magnet Synchronous Motor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Liang%20Qin">Liang Qin</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanan%20M.%20D.%20Habbi"> Hanan M. D. Habbi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> A robust sensorless speed for permanent magnet synchronous motor (PMSM) has been presented for estimation of stator flux components and rotor speed based on The Extended Kalman Filter (EKF). The model of PMSM and its EKF models are modeled in Matlab /Sirnulink environment. The proposed EKF speed estimation method is also proved insensitive to the PMSM parameter variations. Simulation results demonstrate a good performance and robustness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DTC" title="DTC">DTC</a>, <a href="https://publications.waset.org/abstracts/search?q=Extended%20Kalman%20Filter%20%28EKF%29" title=" Extended Kalman Filter (EKF)"> Extended Kalman Filter (EKF)</a>, <a href="https://publications.waset.org/abstracts/search?q=PMSM" title=" PMSM"> PMSM</a>, <a href="https://publications.waset.org/abstracts/search?q=sensorless%20control" title=" sensorless control"> sensorless control</a>, <a href="https://publications.waset.org/abstracts/search?q=anti-windup%20PI" title=" anti-windup PI"> anti-windup PI</a> </p> <a href="https://publications.waset.org/abstracts/22472/extended-kalman-filter-based-direct-torque-control-of-permanent-magnet-synchronous-motor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22472.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">664</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">421</span> Speech Enhancement Using Kalman Filter in Communication</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eng.%20Alaa%20K.%20Satti%20Salih">Eng. Alaa K. Satti Salih</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Revolutions Applications such as telecommunications, hands-free communications, recording, etc. which need at least one microphone, the signal is usually infected by noise and echo. The important application is the speech enhancement, which is done to remove suppressed noises and echoes taken by a microphone, beside preferred speech. Accordingly, the microphone signal has to be cleaned using digital signal processing DSP tools before it is played out, transmitted, or stored. Engineers have so far tried different approaches to improving the speech by get back the desired speech signal from the noisy observations. Especially Mobile communication, so in this paper will do reconstruction of the speech signal, observed in additive background noise, using the Kalman filter technique to estimate the parameters of the Autoregressive Process (AR) in the state space model and the output speech signal obtained by the MATLAB. The accurate estimation by Kalman filter on speech would enhance and reduce the noise then compare and discuss the results between actual values and estimated values which produce the reconstructed signals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autoregressive%20process" title="autoregressive process">autoregressive process</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=Matlab" title=" Matlab"> Matlab</a>, <a href="https://publications.waset.org/abstracts/search?q=noise%20speech" title=" noise speech"> noise speech</a> </p> <a href="https://publications.waset.org/abstracts/7182/speech-enhancement-using-kalman-filter-in-communication" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7182.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">344</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">420</span> Image Enhancement Algorithm of Photoacoustic Tomography Using Active Contour Filtering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prasannakumar%20Palaniappan">Prasannakumar Palaniappan</a>, <a href="https://publications.waset.org/abstracts/search?q=Dong%20Ho%20Shin"> Dong Ho Shin</a>, <a href="https://publications.waset.org/abstracts/search?q=Chul%20Gyu%20Song"> Chul Gyu Song</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The photoacoustic images are obtained from a custom developed linear array photoacoustic tomography system. The biological specimens are imitated by conducting phantom tests in order to retrieve a fully functional photoacoustic image. The acquired image undergoes the active region based contour filtering to remove the noise and accurately segment the object area for further processing. The universal back projection method is used as the image reconstruction algorithm. The active contour filtering is analyzed by evaluating the signal to noise ratio and comparing it with the other filtering methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=contour%20filtering" title="contour filtering">contour filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20array" title=" linear array"> linear array</a>, <a href="https://publications.waset.org/abstracts/search?q=photoacoustic%20tomography" title=" photoacoustic tomography"> photoacoustic tomography</a>, <a href="https://publications.waset.org/abstracts/search?q=universal%20back%20projection" title=" universal back projection"> universal back projection</a> </p> <a href="https://publications.waset.org/abstracts/40626/image-enhancement-algorithm-of-photoacoustic-tomography-using-active-contour-filtering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40626.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">400</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">419</span> Real-Time Visualization Using GPU-Accelerated Filtering of LiDAR Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sa%C5%A1o%20Pe%C4%8Dnik">Sašo Pečnik</a>, <a href="https://publications.waset.org/abstracts/search?q=Borut%20%C5%BDalik"> Borut Žalik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a real-time visualization technique and filtering of classified LiDAR point clouds. The visualization is capable of displaying filtered information organized in layers by the classification attribute saved within LiDAR data sets. We explain the used data structure and data management, which enables real-time presentation of layered LiDAR data. Real-time visualization is achieved with LOD optimization based on the distance from the observer without loss of quality. The filtering process is done in two steps and is entirely executed on the GPU and implemented using programmable shaders. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=filtering" title="filtering">filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=graphics" title=" graphics"> graphics</a>, <a href="https://publications.waset.org/abstracts/search?q=level-of-details" title=" level-of-details"> level-of-details</a>, <a href="https://publications.waset.org/abstracts/search?q=LiDAR" title=" LiDAR"> LiDAR</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20visualization" title=" real-time visualization"> real-time visualization</a> </p> <a href="https://publications.waset.org/abstracts/16857/real-time-visualization-using-gpu-accelerated-filtering-of-lidar-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16857.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">308</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">418</span> Tuning of Kalman Filter Using Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hesham%20Abdin">Hesham Abdin</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Zakaria"> Mohamed Zakaria</a>, <a href="https://publications.waset.org/abstracts/search?q=Talaat%20Abd-Elmonaem"> Talaat Abd-Elmonaem</a>, <a href="https://publications.waset.org/abstracts/search?q=Alaa%20El-Din%20Sayed%20Hafez"> Alaa El-Din Sayed Hafez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Kalman filter algorithm is an estimator known as the workhorse of estimation. It has an important application in missile guidance, especially in lack of accurate data of the target due to noise or uncertainty. In this paper, a Kalman filter is used as a tracking filter in a simulated target-interceptor scenario with noise. It estimates the position, velocity, and acceleration of the target in the presence of noise. These estimations are needed for both proportional navigation and differential geometry guidance laws. A Kalman filter has a good performance at low noise, but a large noise causes considerable errors leads to performance degradation. Therefore, a new technique is required to overcome this defect using tuning factors to tune a Kalman filter to adapt increasing of noise. The values of the tuning factors are between 0.8 and 1.2, they have a specific value for the first half of range and a different value for the second half. they are multiplied by the estimated values. These factors have its optimum values and are altered with the change of the target heading. A genetic algorithm updates these selections to increase the maximum effective range which was previously reduced by noise. The results show that the selected factors have other benefits such as decreasing the minimum effective range that was increased earlier due to noise. In addition to, the selected factors decrease the miss distance for all ranges of this direction of the target, and expand the effective range which leads to increase probability of kill. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=proportional%20navigation" title="proportional navigation">proportional navigation</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%20geometry" title=" differential geometry"> differential geometry</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a> </p> <a href="https://publications.waset.org/abstracts/21005/tuning-of-kalman-filter-using-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21005.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">510</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">417</span> Depth Camera Aided Dead-Reckoning Localization of Autonomous Mobile Robots in Unstructured GNSS-Denied Environments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=David%20L.%20Olson">David L. Olson</a>, <a href="https://publications.waset.org/abstracts/search?q=Stephen%20B.%20H.%20Bruder"> Stephen B. H. Bruder</a>, <a href="https://publications.waset.org/abstracts/search?q=Adam%20S.%20Watkins"> Adam S. Watkins</a>, <a href="https://publications.waset.org/abstracts/search?q=Cleon%20E.%20Davis"> Cleon E. Davis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In global navigation satellite systems (GNSS), denied settings such as indoor environments, autonomous mobile robots are often limited to dead-reckoning navigation techniques to determine their position, velocity, and attitude (PVA). Localization is typically accomplished by employing an inertial measurement unit (IMU), which, while precise in nature, accumulates errors rapidly and severely degrades the localization solution. Standard sensor fusion methods, such as Kalman filtering, aim to fuse precise IMU measurements with accurate aiding sensors to establish a precise and accurate solution. In indoor environments, where GNSS and no other a priori information is known about the environment, effective sensor fusion is difficult to achieve, as accurate aiding sensor choices are sparse. However, an opportunity arises by employing a depth camera in the indoor environment. A depth camera can capture point clouds of the surrounding floors and walls. Extracting attitude from these surfaces can serve as an accurate aiding source, which directly combats errors that arise due to gyroscope imperfections. This configuration for sensor fusion leads to a dramatic reduction of PVA error compared to traditional aiding sensor configurations. This paper provides the theoretical basis for the depth camera aiding sensor method, initial expectations of performance benefit via simulation, and hardware implementation, thus verifying its veracity. Hardware implementation is performed on the Quanser Qbot 2™ mobile robot, with a Vector-Nav VN-200™ IMU and Kinect™ camera from Microsoft. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autonomous%20mobile%20robotics" title="autonomous mobile robotics">autonomous mobile robotics</a>, <a href="https://publications.waset.org/abstracts/search?q=dead%20reckoning" title=" dead reckoning"> dead reckoning</a>, <a href="https://publications.waset.org/abstracts/search?q=depth%20camera" title=" depth camera"> depth camera</a>, <a href="https://publications.waset.org/abstracts/search?q=inertial%20navigation" title=" inertial navigation"> inertial navigation</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filtering" title=" Kalman filtering"> Kalman filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=localization" title=" localization"> localization</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20fusion" title=" sensor fusion"> sensor fusion</a> </p> <a href="https://publications.waset.org/abstracts/134870/depth-camera-aided-dead-reckoning-localization-of-autonomous-mobile-robots-in-unstructured-gnss-denied-environments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134870.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">207</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">416</span> 3D Guided Image Filtering to Improve Quality of Short-Time Binned Dynamic PET Images Using MRI Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tabassum%20Husain">Tabassum Husain</a>, <a href="https://publications.waset.org/abstracts/search?q=Shen%20Peng%20Li"> Shen Peng Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhaolin%20Chen"> Zhaolin Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper evaluates the usability of 3D Guided Image Filtering to enhance the quality of short-time binned dynamic PET images by using MRI images. Guided image filtering is an edge-preserving filter proposed to enhance 2D images. The 3D filter is applied on 1 and 5-minute binned images. The results are compared with 15-minute binned images and the Gaussian filtering. The guided image filter enhances the quality of dynamic PET images while also preserving important information of the voxels. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20PET%20images" title="dynamic PET images">dynamic PET images</a>, <a href="https://publications.waset.org/abstracts/search?q=guided%20image%20filter" title=" guided image filter"> guided image filter</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20enhancement" title=" image enhancement"> image enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20preservation%20filtering" title=" information preservation filtering"> information preservation filtering</a> </p> <a href="https://publications.waset.org/abstracts/152864/3d-guided-image-filtering-to-improve-quality-of-short-time-binned-dynamic-pet-images-using-mri-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152864.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">132</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">415</span> Linear MIMO Model Identification Using an Extended Kalman Filter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Matthew%20C.%20Best">Matthew C. Best</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Linear Multi-Input Multi-Output (MIMO) dynamic models can be identified, with no a priori knowledge of model structure or order, using a new Generalised Identifying Filter (GIF). Based on an Extended Kalman Filter, the new filter identifies the model iteratively, in a continuous modal canonical form, using only input and output time histories. The filter’s self-propagating state error covariance matrix allows easy determination of convergence and conditioning, and by progressively increasing model order, the best fitting reduced-order model can be identified. The method is shown to be resistant to noise and can easily be extended to identification of smoothly nonlinear systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=system%20identification" title="system identification">system identification</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20model" title=" linear model"> linear model</a>, <a href="https://publications.waset.org/abstracts/search?q=MIMO" title=" MIMO"> MIMO</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20order%20reduction" title=" model order reduction"> model order reduction</a> </p> <a href="https://publications.waset.org/abstracts/24532/linear-mimo-model-identification-using-an-extended-kalman-filter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24532.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">594</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">414</span> Application of Artificial Immune Systems Combined with Collaborative Filtering in Movie Recommendation System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pei-Chann%20Chang">Pei-Chann Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jhen-Fu%20Liao"> Jhen-Fu Liao</a>, <a href="https://publications.waset.org/abstracts/search?q=Chin-Hung%20Teng"> Chin-Hung Teng</a>, <a href="https://publications.waset.org/abstracts/search?q=Meng-Hui%20Chen"> Meng-Hui Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research combines artificial immune system with user and item based collaborative filtering to create an efficient and accurate recommendation system. By applying the characteristic of antibodies and antigens in the artificial immune system and using Pearson correlation coefficient as the affinity threshold to cluster the data, our collaborative filtering can effectively find useful users and items for rating prediction. This research uses MovieLens dataset as our testing target to evaluate the effectiveness of the algorithm developed in this study. The experimental results show that the algorithm can effectively and accurately predict the movie ratings. Compared to some state of the art collaborative filtering systems, our system outperforms them in terms of the mean absolute error on the MovieLens dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20immune%20system" title="artificial immune system">artificial immune system</a>, <a href="https://publications.waset.org/abstracts/search?q=collaborative%20filtering" title=" collaborative filtering"> collaborative filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=recommendation%20system" title=" recommendation system"> recommendation system</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity" title=" similarity"> similarity</a> </p> <a href="https://publications.waset.org/abstracts/5057/application-of-artificial-immune-systems-combined-with-collaborative-filtering-in-movie-recommendation-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5057.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">535</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">413</span> Speed up Vector Median Filtering by Quasi Euclidean Norm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vinai%20K.%20Singh">Vinai K. Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For reducing impulsive noise without degrading image contours, median filtering is a powerful tool. In multiband images as for example colour images or vector fields obtained by optic flow computation, a vector median filter can be used. Vector median filters are defined on the basis of a suitable distance, the best performing distance being the Euclidean. Euclidean distance is evaluated by using the Euclidean norms which is quite demanding from the point of view of computation given that a square root is required. In this paper an optimal piece-wise linear approximation of the Euclidean norm is presented which is applied to vector median filtering. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=euclidean%20norm" title="euclidean norm">euclidean norm</a>, <a href="https://publications.waset.org/abstracts/search?q=quasi%20euclidean%20norm" title=" quasi euclidean norm"> quasi euclidean norm</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20median%20filtering" title=" vector median filtering"> vector median filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=applied%20mathematics" title=" applied mathematics"> applied mathematics</a> </p> <a href="https://publications.waset.org/abstracts/21942/speed-up-vector-median-filtering-by-quasi-euclidean-norm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21942.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">474</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">412</span> Building and Tree Detection Using Multiscale Matched Filtering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdullah%20H.%20%C3%96zcan">Abdullah H. Özcan</a>, <a href="https://publications.waset.org/abstracts/search?q=Dilara%20Hisar"> Dilara Hisar</a>, <a href="https://publications.waset.org/abstracts/search?q=Yetkin%20Sayar"> Yetkin Sayar</a>, <a href="https://publications.waset.org/abstracts/search?q=Cem%20%C3%9Cnsalan"> Cem Ünsalan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, an automated building and tree detection method is proposed using DSM data and true orthophoto image. A multiscale matched filtering is used on DSM data. Therefore, first watershed transform is applied. Then, Otsu’s thresholding method is used as an adaptive threshold to segment each watershed region. Detected objects are masked with NDVI to separate buildings and trees. The proposed method is able to detect buildings and trees without entering any elevation threshold. We tested our method on ISPRS semantic labeling dataset and obtained promising results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=building%20detection" title="building detection">building detection</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20maximum%20filtering" title=" local maximum filtering"> local maximum filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=matched%20filtering" title=" matched filtering"> matched filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=multiscale" title=" multiscale"> multiscale</a> </p> <a href="https://publications.waset.org/abstracts/59277/building-and-tree-detection-using-multiscale-matched-filtering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59277.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">320</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">411</span> Kalman Filter Design in Structural Identification with Unknown Excitation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Z.%20Masoumi">Z. Masoumi</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Moaveni"> B. Moaveni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article is about first step of structural health monitoring by identifying structural system in the presence of unknown input. In the structural system identification, identification of structural parameters such as stiffness and damping are considered. In this study, the Kalman filter (KF) design for structural systems with unknown excitation is expressed. External excitations, such as earthquakes, wind or any other forces are not measured or not available. The purpose of this filter is its strengths to estimate the state variables of the system in the presence of unknown input. Also least squares estimation (LSE) method with unknown input is studied. Estimates of parameters have been adopted. Finally, using two examples advantages and drawbacks of both methods are studied. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter%20%28KF%29" title="Kalman filter (KF)">Kalman filter (KF)</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20square%20estimation%20%28LSE%29" title=" least square estimation (LSE)"> least square estimation (LSE)</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20health%20monitoring%20%28SHM%29" title=" structural health monitoring (SHM)"> structural health monitoring (SHM)</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20system%20identification" title=" structural system identification"> structural system identification</a> </p> <a href="https://publications.waset.org/abstracts/49817/kalman-filter-design-in-structural-identification-with-unknown-excitation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49817.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">317</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Kalman%20filtering&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Kalman%20filtering&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Kalman%20filtering&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Kalman%20filtering&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Kalman%20filtering&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Kalman%20filtering&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Kalman%20filtering&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Kalman%20filtering&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Kalman%20filtering&page=10">10</a></li> <li class="page-item disabled"><span class="page-link">...</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Kalman%20filtering&page=14">14</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Kalman%20filtering&page=15">15</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Kalman%20filtering&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>