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filter gain</title> <meta name="description" content="Search results for: Kalman filter gain"> <meta name="keywords" content="Kalman filter gain"> <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 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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 filter gain"> <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> 2620</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Kalman filter gain</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2620</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">2619</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">2618</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">2617</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">2616</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">2615</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">2614</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">2613</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">2612</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">2611</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">2610</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">2609</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> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2608</span> Adaptive Kaman Filter for Fault Diagnosis of Linear Parameter-Varying Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rajamani%20Doraiswami">Rajamani Doraiswami</a>, <a href="https://publications.waset.org/abstracts/search?q=Lahouari%20Cheded"> Lahouari Cheded</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fault diagnosis of Linear Parameter-Varying (LPV) system using an adaptive Kalman filter is proposed. The LPV model is comprised of scheduling parameters, and the emulator parameters. The scheduling parameters are chosen such that they are capable of tracking variations in the system model as a result of changes in the operating regimes. The emulator parameters, on the other hand, simulate variations in the subsystems during the identification phase and have negligible effect during the operational phase. The nominal model and the influence vectors, which are the gradient of the feature vector respect to the emulator parameters, are identified off-line from a number of emulator parameter perturbed experiments. A Kalman filter is designed using the identified nominal model. As the system varies, the Kalman filter model is adapted using the scheduling variables. The residual is employed for fault diagnosis. The proposed scheme is successfully evaluated on simulated system as well as on a physical process control system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=identification" title="identification">identification</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20parameter-varying%20systems" title=" linear parameter-varying systems"> linear parameter-varying systems</a>, <a href="https://publications.waset.org/abstracts/search?q=least-squares%20estimation" title=" least-squares estimation"> least-squares estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title=" fault diagnosis"> fault diagnosis</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=emulators" title=" emulators"> emulators</a> </p> <a href="https://publications.waset.org/abstracts/7656/adaptive-kaman-filter-for-fault-diagnosis-of-linear-parameter-varying-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7656.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">499</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">2607</span> A Packet Loss Probability Estimation Filter Using Most Recent Finite Traffic Measurements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pyung%20Soo%20Kim">Pyung Soo Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Eung%20Hyuk%20Lee"> Eung Hyuk Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Mun%20Suck%20Jang"> Mun Suck Jang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A packet loss probability (PLP) estimation filter with finite memory structure is proposed to estimate the packet rate mean and variance of the input traffic process in real-time while removing undesired system and measurement noises. The proposed PLP estimation filter is developed under a weighted least square criterion using only the finite traffic measurements on the most recent window. The proposed PLP estimation filter is shown to have several inherent properties such as unbiasedness, deadbeat, robustness. A guideline for choosing appropriate window length is described since it can affect significantly the estimation performance. Using computer simulations, the proposed PLP estimation filter is shown to be superior to the Kalman filter for the temporarily uncertain system. One possible explanation for this is that the proposed PLP estimation filter can have greater convergence time of a filtered estimate as the window length M decreases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=packet%20loss%20probability%20estimation" title="packet loss probability estimation">packet loss probability estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20memory%20filter" title=" finite memory filter"> finite memory filter</a>, <a href="https://publications.waset.org/abstracts/search?q=infinite%20memory%20filter" title=" infinite memory filter"> infinite memory filter</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a> </p> <a href="https://publications.waset.org/abstracts/9519/a-packet-loss-probability-estimation-filter-using-most-recent-finite-traffic-measurements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9519.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">672</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">2606</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">2605</span> State Estimation of a Biotechnological Process Using Extended Kalman Filter and Particle Filter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Simutis">R. Simutis</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Galvanauskas"> V. Galvanauskas</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Levisauskas"> D. Levisauskas</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Repsyte"> J. Repsyte</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Grincas"> V. Grincas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with advanced state estimation algorithms for estimation of biomass concentration and specific growth rate in a typical fed-batch biotechnological process. This biotechnological process was represented by a nonlinear mass-balance based process model. Extended Kalman Filter (EKF) and Particle Filter (PF) was used to estimate the unmeasured state variables from oxygen uptake rate (OUR) and base consumption (BC) measurements. To obtain more general results, a simplified process model was involved in EKF and PF estimation algorithms. This model doesn’t require any special growth kinetic equations and could be applied for state estimation in various bioprocesses. The focus of this investigation was concentrated on the comparison of the estimation quality of the EKF and PF estimators by applying different measurement noises. The simulation results show that Particle Filter algorithm requires significantly more computation time for state estimation but gives lower estimation errors both for biomass concentration and specific growth rate. Also the tuning procedure for Particle Filter is simpler than for EKF. Consequently, Particle Filter should be preferred in real applications, especially for monitoring of industrial bioprocesses where the simplified implementation procedures are always desirable. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biomass%20concentration" title="biomass concentration">biomass concentration</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=particle%20filter" title=" particle filter"> particle filter</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=specific%20growth%20rate" title=" specific growth rate"> specific growth rate</a> </p> <a href="https://publications.waset.org/abstracts/12940/state-estimation-of-a-biotechnological-process-using-extended-kalman-filter-and-particle-filter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12940.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">428</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">2604</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">2603</span> Performance of BLDC Motor under Kalman Filter Sensorless Drive</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuri%20Boiko">Yuri Boiko</a>, <a href="https://publications.waset.org/abstracts/search?q=Ci%20Lin"> Ci Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Iluju%20Kiringa"> Iluju Kiringa</a>, <a href="https://publications.waset.org/abstracts/search?q=Tet%20Yeap"> Tet Yeap</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The performance of a BLDC motor controlled by the Kalman filter-based position-sensorless drive is studied in terms of its dependence on the system’s parameters' variations. The effects of system’s parameters changes on the dynamic behavior of state variables are verified. Simulated is a closed-loop control scheme with a Kalman filter in the feedback line. Distinguished are two separate data sampling modes in analyzing feedback output from the BLDC motor: (1) equal angular separation and (2) equal time intervals. In case (1), the data are collected via equal intervals Δθ of rotor’s angular position θᵢ, i.e., keeping Δθ=const. In case (2), the data collection time points tᵢ are separated by equal sampling time intervals Δt=const. Demonstrated are the effects of the parameters changes on the sensorless control flow, in particular, reduction of the torque ripples, switching spikes, torque load balancing. It is specifically shown that an efficient suppression of commutation induced torque ripples is achievable selection of the sampling rate in the Kalman filter settings above certain critical value. The computational cost of such suppression is shown to be higher for the motors with lower induction values of the windings. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BLDC%20motor" title="BLDC motor">BLDC motor</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=sensorless%20drive" title=" sensorless drive"> sensorless drive</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20variables" title=" state variables"> state variables</a>, <a href="https://publications.waset.org/abstracts/search?q=torque%20ripples%20reduction" title=" torque ripples reduction"> torque ripples reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=sampling%20rate" title=" sampling rate"> sampling rate</a> </p> <a href="https://publications.waset.org/abstracts/128662/performance-of-bldc-motor-under-kalman-filter-sensorless-drive" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128662.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">148</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">2602</span> Function Approximation with Radial Basis Function Neural Networks via FIR Filter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kyu%20Chul%20Lee">Kyu Chul Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Sung%20Hyun%20Yoo"> Sung Hyun Yoo</a>, <a href="https://publications.waset.org/abstracts/search?q=Choon%20Ki%20Ahn"> Choon Ki Ahn</a>, <a href="https://publications.waset.org/abstracts/search?q=Myo%20Taeg%20Lim"> Myo Taeg Lim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recent experimental evidences have shown that because of a fast convergence and a nice accuracy, neural networks training via extended Kalman filter (EKF) method is widely applied. However, as to an uncertainty of the system dynamics or modeling error, the performance of the method is unreliable. In order to overcome this problem in this paper, a new finite impulse response (FIR) filter based learning algorithm is proposed to train radial basis function neural networks (RBFN) for nonlinear function approximation. Compared to the EKF training method, the proposed FIR filter training method is more robust to those environmental conditions. Furthermore, the number of centers will be considered since it affects the performance of approximation. <p class="card-text"><strong>Keywords:</strong> <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=classification%20problem" title=" classification problem"> classification problem</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20networks%20%28RBFN%29" title=" radial basis function networks (RBFN)"> radial basis function networks (RBFN)</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20impulse%20response%20%28FIR%29%20filter" title=" finite impulse response (FIR) filter"> finite impulse response (FIR) filter</a> </p> <a href="https://publications.waset.org/abstracts/13851/function-approximation-with-radial-basis-function-neural-networks-via-fir-filter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13851.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">456</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">2601</span> Investigating Activity Recognition Using 9-Axis Sensors and Filters in Wearable Devices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jun%20Gil%20Ahn">Jun Gil Ahn</a>, <a href="https://publications.waset.org/abstracts/search?q=Jong%20Kang%20Park"> Jong Kang Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Jong%20Tae%20Kim"> Jong Tae Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we analyze major components of activity recognition (AR) in wearable device with 9-axis sensors and sensor fusion filters. 9-axis sensors commonly include 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. We chose sensor fusion filters as Kalman filter and Direction Cosine Matrix (DCM) filter. We also construct sensor fusion data from each activity sensor data and perform classification by accuracy of AR using Naïve Bayes and SVM. According to the classification results, we observed that the DCM filter and the specific combination of the sensing axes are more effective for AR in wearable devices while classifying walking, running, ascending and descending. <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=activity%20recognition" title=" activity recognition"> activity recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=directiona%20cosine%20matrix%20filter" title=" directiona cosine matrix filter"> directiona cosine matrix filter</a>, <a href="https://publications.waset.org/abstracts/search?q=gyroscope" title=" gyroscope"> gyroscope</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=magnetometer" title=" magnetometer"> magnetometer</a> </p> <a href="https://publications.waset.org/abstracts/56198/investigating-activity-recognition-using-9-axis-sensors-and-filters-in-wearable-devices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56198.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">333</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">2600</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">2599</span> OFDM Radar for High Accuracy Target Tracking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahbube%20Eghtesad">Mahbube Eghtesad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For a number of years, the problem of simultaneous detection and tracking of a target has been one of the most relevant and challenging issues in a wide variety of military and civilian systems. We develop methods for detecting and tracking a target using an orthogonal frequency division multiplexing (OFDM) based radar. As a preliminary step we introduce the target trajectory and Gaussian noise model in discrete time form. Then resorting to match filter and Kalman filter we derive a detector and target tracker. After that we propose an OFDM radar in order to achieve further improvement in tracking performance. The motivation for employing multiple frequencies is that the different scattering centers of a target resonate differently at each frequency. Numerical examples illustrate our analytical results, demonstrating the achieved performance improvement due to the OFDM signaling method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=matched%20filter" title="matched filter">matched filter</a>, <a href="https://publications.waset.org/abstracts/search?q=target%20trashing" title=" target trashing"> target trashing</a>, <a href="https://publications.waset.org/abstracts/search?q=OFDM%20radar" title=" OFDM radar"> OFDM radar</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a> </p> <a href="https://publications.waset.org/abstracts/8926/ofdm-radar-for-high-accuracy-target-tracking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8926.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">398</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">2598</span> Model Predictive Control with Unscented Kalman Filter for Nonlinear Implicit Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Takashi%20Shimizu">Takashi Shimizu</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto"> Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A class of implicit systems is known as a more generalized class of systems than a class of explicit systems. To establish a control method for such a generalized class of systems, we adopt model predictive control method which is a kind of optimal feedback control with a performance index that has a moving initial time and terminal time. However, model predictive control method is inapplicable to systems whose all state variables are not exactly known. In other words, model predictive control method is inapplicable to systems with limited measurable states. In fact, it is usual that the state variables of systems are measured through outputs, hence, only limited parts of them can be used directly. It is also usual that output signals are disturbed by process and sensor noises. Hence, it is important to establish a state estimation method for nonlinear implicit systems with taking the process noise and sensor noise into consideration. To this purpose, we apply the model predictive control method and unscented Kalman filter for solving the optimization and estimation problems of nonlinear implicit systems, respectively. The objective of this study is to establish a model predictive control with unscented Kalman filter for nonlinear implicit systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title="optimal control">optimal control</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%20estimation" title=" state estimation"> state estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a> </p> <a href="https://publications.waset.org/abstracts/97739/model-predictive-control-with-unscented-kalman-filter-for-nonlinear-implicit-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97739.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">202</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">2597</span> Segmenting 3D Optical Coherence Tomography Images Using a Kalman Filter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Deniz%20Guven">Deniz Guven</a>, <a href="https://publications.waset.org/abstracts/search?q=Wil%20Ward"> Wil Ward</a>, <a href="https://publications.waset.org/abstracts/search?q=Jinming%20Duan"> Jinming Duan</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Bai"> Li Bai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over the past two decades or so, Optical Coherence Tomography (OCT) has been used to diagnose retina and optic nerve diseases. The retinal nerve fibre layer, for example, is a powerful diagnostic marker for detecting and staging glaucoma. With the advances in optical imaging hardware, the adoption of OCT is now commonplace in clinics. More and more OCT images are being generated, and for these OCT images to have clinical applicability, accurate automated OCT image segmentation software is needed. Oct image segmentation is still an active research area, as OCT images are inherently noisy, with the multiplicative speckling noise. Simple edge detection algorithms are unsuitable for detecting retinal layer boundaries in OCT images. Intensity fluctuation, motion artefact, and the presence of blood vessels also decrease further OCT image quality. In this paper, we introduce a new method for segmenting three-dimensional (3D) OCT images. This involves the use of a Kalman filter, which is commonly used in computer vision for object tracking. The Kalman filter is applied to the 3D OCT image volume to track the retinal layer boundaries through the slices within the volume and thus segmenting the 3D image. Specifically, after some pre-processing of the OCT images, points on the retinal layer boundaries in the first image are identified, and curve fitting is applied to them such that the layer boundaries can be represented by the coefficients of the curve equations. These coefficients then form the state space for the Kalman Filter. The filter then produces an optimal estimate of the current state of the system by updating its previous state using the measurements available in the form of a feedback control loop. The results show that the algorithm can be used to segment the retinal layers in OCT images. One of the limitations of the current algorithm is that the curve representation of the retinal layer boundary does not work well when the layer boundary is split into two, e.g., at the optic nerve, the layer boundary split into two. This maybe resolved by using a different approach to representing the boundaries, such as b-splines or level sets. The use of a Kalman filter shows promise to developing accurate and effective 3D OCT segmentation methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optical%20coherence%20tomography" title="optical coherence tomography">optical coherence tomography</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title=" image segmentation"> image segmentation</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=object%20tracking" title=" object tracking"> object tracking</a> </p> <a href="https://publications.waset.org/abstracts/66524/segmenting-3d-optical-coherence-tomography-images-using-a-kalman-filter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66524.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">482</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">2596</span> State Estimation Method Based on Unscented Kalman Filter for Vehicle Nonlinear Dynamics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wataru%20Nakamura">Wataru Nakamura</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto"> Tomoaki Hashimoto</a>, <a href="https://publications.waset.org/abstracts/search?q=Liang-Kuang%20Chen"> Liang-Kuang Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper provides a state estimation method for automatic control systems of nonlinear vehicle dynamics. A nonlinear tire model is employed to represent the realistic behavior of a vehicle. In general, all the state variables of control systems are not precisedly known, because those variables are observed through output sensors and limited parts of them might be only measurable. Hence, automatic control systems must incorporate some type of state estimation. It is needed to establish a state estimation method for nonlinear vehicle dynamics with restricted measurable state variables. For this purpose, unscented Kalman filter method is applied in this study for estimating the state variables of nonlinear vehicle dynamics. The objective of this paper is to propose a state estimation method using unscented Kalman filter for nonlinear vehicle dynamics. The effectiveness of the proposed method is verified by numerical simulations. <p class="card-text"><strong>Keywords:</strong> <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=control%20systems" title=" control systems"> control systems</a>, <a href="https://publications.waset.org/abstracts/search?q=observer%20systems" title=" observer systems"> observer systems</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20systems" title=" nonlinear systems"> nonlinear systems</a> </p> <a href="https://publications.waset.org/abstracts/118764/state-estimation-method-based-on-unscented-kalman-filter-for-vehicle-nonlinear-dynamics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118764.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">134</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">2595</span> A Finite Memory Residual Generation Filter for Fault Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pyung%20Soo%20Kim">Pyung Soo Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Eung%20Hyuk%20Lee"> Eung Hyuk Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Mun%20Suck%20Jang"> Mun Suck Jang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the current paper, a residual generation filter with finite memory structure is proposed for fault detection. The proposed finite memory residual generation filter provides the residual by real-time filtering of fault vector using only the most recent finite observations and inputs on the window. It is shown that the residual given by the proposed residual generation filter provides the exact fault for noise-free systems. Finally, to illustrate the capability of the proposed residual generation filter, numerical examples are performed for the discretized DC motor system having the multiple sensor faults. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=residual%20generation%20filter" title="residual generation filter">residual generation filter</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20memory%20structure" title=" finite memory structure"> finite memory structure</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=fast%20detection" title=" fast detection"> fast detection</a> </p> <a href="https://publications.waset.org/abstracts/35140/a-finite-memory-residual-generation-filter-for-fault-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35140.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">698</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">2594</span> Comparison of Loosely Coupled and Tightly Coupled INS/GNSS Architecture for Guided Rocket Navigation System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rahmat%20Purwoko">Rahmat Purwoko</a>, <a href="https://publications.waset.org/abstracts/search?q=Bambang%20Riyanto%20Trilaksono"> Bambang Riyanto Trilaksono</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper gives comparison of INS/GNSS architecture namely Loosely Coupled and Tightly Coupled using Hardware in the Loop Simulation in Guided Missile RKX-200 rocket model. INS/GNSS Tightly Coupled architecture requires pseudo-range, pseudo-range rate, and position and velocity of each satellite in constellation from GPS (Global Positioning System) measurement. The Loosely Coupled architecture use estimated position and velocity from GNSS receiver. INS/GNSS architecture also requires angular rate and specific force measurement from IMU (Inertial Measurement Unit). Loosely Coupled arhitecture designed using 15 states Kalman Filter and Tightly Coupled designed using 17 states Kalman Filter. Integration algorithm calculation using ECEF frame. Navigation System implemented Zedboard All Programmable SoC. <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=loosely%20coupled" title=" loosely coupled"> loosely coupled</a>, <a href="https://publications.waset.org/abstracts/search?q=navigation%20system" title=" navigation system"> navigation system</a>, <a href="https://publications.waset.org/abstracts/search?q=tightly%20coupled" title=" tightly coupled"> tightly coupled</a> </p> <a href="https://publications.waset.org/abstracts/57097/comparison-of-loosely-coupled-and-tightly-coupled-insgnss-architecture-for-guided-rocket-navigation-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57097.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">309</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">2593</span> State Estimation Based on Unscented Kalman Filter for Burgers’ Equation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Takashi%20Shimizu">Takashi Shimizu</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto"> Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Controlling the flow of fluids is a challenging problem that arises in many fields. Burgers’ equation is a fundamental equation for several flow phenomena such as traffic, shock waves, and turbulence. The optimal feedback control method, so-called model predictive control, has been proposed for Burgers’ equation. However, the model predictive control method is inapplicable to systems whose all state variables are not exactly known. In practical point of view, it is unusual that all the state variables of systems are exactly known, because the state variables of systems are measured through output sensors and limited parts of them can be only available. In fact, it is usual that flow velocities of fluid systems cannot be measured for all spatial domains. Hence, any practical feedback controller for fluid systems must incorporate some type of state estimator. To apply the model predictive control to the fluid systems described by Burgers’ equation, it is needed to establish a state estimation method for Burgers’ equation with limited measurable state variables. To this purpose, we apply unscented Kalman filter for estimating the state variables of fluid systems described by Burgers’ equation. The objective of this study is to establish a state estimation method based on unscented Kalman filter for Burgers’ equation. The effectiveness of the proposed method is verified by numerical simulations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=observer%20systems" title="observer systems">observer systems</a>, <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=nonlinear%20systems" title=" nonlinear systems"> nonlinear systems</a>, <a href="https://publications.waset.org/abstracts/search?q=Burgers%27%20equation" title=" Burgers' equation"> Burgers' equation</a> </p> <a href="https://publications.waset.org/abstracts/99541/state-estimation-based-on-unscented-kalman-filter-for-burgers-equation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99541.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">153</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">2592</span> Estimation of the Temperatures in an Asynchronous Machine Using Extended Kalman Filter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yi%20Huang">Yi Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Clemens%20Guehmann"> Clemens Guehmann</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to monitor the thermal behavior of an asynchronous machine with squirrel cage rotor, a 9th-order extended Kalman filter (EKF) algorithm is implemented to estimate the temperatures of the stator windings, the rotor cage and the stator core. The state-space equations of EKF are established based on the electrical, mechanical and the simplified thermal models of an asynchronous machine. The asynchronous machine with simplified thermal model in Dymola is compiled as DymolaBlock, a physical model in MATLAB/Simulink. The coolant air temperature, three-phase voltages and currents are exported from the physical model and are processed by EKF estimator as inputs. Compared to the temperatures exported from the physical model of the machine, three parts of temperatures can be estimated quite accurately by the EKF estimator. The online EKF estimator is independent from the machine control algorithm and can work under any speed and load condition if the stator current is nonzero current system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=asynchronous%20machine" title="asynchronous machine">asynchronous machine</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=resistance" title=" resistance"> resistance</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=temperature%20estimation" title=" temperature estimation"> temperature estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=thermal%20model" title=" thermal model"> thermal model</a> </p> <a href="https://publications.waset.org/abstracts/67431/estimation-of-the-temperatures-in-an-asynchronous-machine-using-extended-kalman-filter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67431.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">285</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">2591</span> Relative Navigation with Laser-Based Intermittent Measurement for Formation Flying Satellites</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jongwoo%20Lee">Jongwoo Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Dae-Eun%20Kang"> Dae-Eun Kang</a>, <a href="https://publications.waset.org/abstracts/search?q=Sang-Young%20Park"> Sang-Young Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study presents a precise relative navigational method for satellites flying in formation using laser-based intermittent measurement data. The measurement data for the relative navigation between two satellites consist of a relative distance measured by a laser instrument and relative attitude angles measured by attitude determination. The relative navigation solutions are estimated by both the Extended Kalman filter (EKF) and unscented Kalman filter (UKF). The solutions estimated by the EKF may become inaccurate or even diverge as measurement outage time gets longer because the EKF utilizes a linearization approach. However, this study shows that the UKF with the appropriate scaling parameters provides a stable and accurate relative navigation solutions despite the long measurement outage time and large initial error as compared to the relative navigation solutions of the EKF. Various navigation results have been analyzed by adjusting the scaling parameters of the UKF. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=satellite%20relative%20navigation" title="satellite relative navigation">satellite relative navigation</a>, <a href="https://publications.waset.org/abstracts/search?q=laser-based%20measurement" title=" laser-based measurement"> laser-based measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=intermittent%20measurement" title=" intermittent measurement"> intermittent measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=unscented%20Kalman%20filter" title=" unscented Kalman filter"> unscented Kalman filter</a> </p> <a href="https://publications.waset.org/abstracts/80146/relative-navigation-with-laser-based-intermittent-measurement-for-formation-flying-satellites" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80146.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">357</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%20filter%20gain&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Kalman%20filter%20gain&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Kalman%20filter%20gain&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Kalman%20filter%20gain&page=5">5</a></li> <li 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