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<meta name="description" content="Search results for: TDOA"> <meta name="keywords" content="TDOA"> <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" 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class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="TDOA"> <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> 7</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: TDOA</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7</span> Determining Coordinates of Ultra-Light Drones Based on the Time Difference of Arrival (TDOA) Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nguyen%20Huy%20Hoang">Nguyen Huy Hoang</a>, <a href="https://publications.waset.org/abstracts/search?q=Do%20Thanh%20Quan"> Do Thanh Quan</a>, <a href="https://publications.waset.org/abstracts/search?q=Tran%20Vu%20Kien"> Tran Vu Kien</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of the active radar to measure the coordinates of ultra-light drones is frequently difficult due to long-distance, absolutely small radar cross-section (RCS) and obstacles. Since ultra-light drones are usually controlled by the Time Difference of Arrival (RF), the paper proposed a method to measure the coordinates of ultra-light drones in the space based on the arrival time of the signal at receiving antennas and the time difference of arrival (TDOA). The experimental results demonstrate that the proposed method is really potential and highly accurate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ultra-light%20drone" title="ultra-light drone">ultra-light drone</a>, <a href="https://publications.waset.org/abstracts/search?q=TDOA" title=" TDOA"> TDOA</a>, <a href="https://publications.waset.org/abstracts/search?q=radar%20cross-section%20%28RCS%29" title=" radar cross-section (RCS)"> radar cross-section (RCS)</a>, <a href="https://publications.waset.org/abstracts/search?q=RF" title=" RF"> RF</a> </p> <a href="https://publications.waset.org/abstracts/147810/determining-coordinates-of-ultra-light-drones-based-on-the-time-difference-of-arrival-tdoa-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147810.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">6</span> Indoor Robot Positioning with Precise Correlation Computations over Walsh-Coded Lightwave Signal Sequences</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jen-Fa%20Huang">Jen-Fa Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu-Wei%20Chiu"> Yu-Wei Chiu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jhe-Ren%20Cheng"> Jhe-Ren Cheng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Visible light communication (VLC) technique has become useful method via LED light blinking. Several issues on indoor mobile robot positioning with LED blinking are examined in the paper. In the transmitter, we control the transceivers blinking message. Orthogonal Walsh codes are adopted for such purpose on auto-correlation function (ACF) to detect signal sequences. In the robot receiver, we set the frame of time by 1 ns passing signal from the transceiver to the mobile robot. After going through many periods of time detecting the peak value of ACF in the mobile robot. Moreover, the transceiver transmits signal again immediately. By capturing three times of peak value, we can know the time difference of arrival (TDOA) between two peak value intervals and finally analyze the accuracy of the robot position. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Visible%20Light%20Communication" title="Visible Light Communication">Visible Light Communication</a>, <a href="https://publications.waset.org/abstracts/search?q=Auto-Correlation%20Function%20%28ACF%29" title=" Auto-Correlation Function (ACF)"> Auto-Correlation Function (ACF)</a>, <a href="https://publications.waset.org/abstracts/search?q=peak%20value%20of%20ACF" title=" peak value of ACF"> peak value of ACF</a>, <a href="https://publications.waset.org/abstracts/search?q=Time%20difference%20of%20Arrival%20%28TDOA%29" title=" Time difference of Arrival (TDOA)"> Time difference of Arrival (TDOA)</a> </p> <a href="https://publications.waset.org/abstracts/55009/indoor-robot-positioning-with-precise-correlation-computations-over-walsh-coded-lightwave-signal-sequences" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55009.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">326</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">5</span> Influence of Scalable Energy-Related Sensor Parameters on Acoustic Localization Accuracy in Wireless Sensor Swarms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Joyraj%20Chakraborty">Joyraj Chakraborty</a>, <a href="https://publications.waset.org/abstracts/search?q=Geoffrey%20Ottoy"> Geoffrey Ottoy</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean-Pierre%20Goemaere"> Jean-Pierre Goemaere</a>, <a href="https://publications.waset.org/abstracts/search?q=Lieven%20De%20Strycker"> Lieven De Strycker</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sensor swarms can be a cost-effectieve and more user-friendly alternative for location based service systems in different application like health-care. To increase the lifetime of such swarm networks, the energy consumption should be scaled to the required localization accuracy. In this paper we have investigated some parameter for energy model that couples localization accuracy to energy-related sensor parameters such as signal length,Bandwidth and sample frequency. The goal is to use the model for the localization of undetermined environmental sounds, by means of wireless acoustic sensors. we first give an overview of TDOA-based localization together with the primary sources of TDOA error (including reverberation effects, Noise). Then we show that in localization, the signal sample rate can be under the Nyquist frequency, provided that enough frequency components remain present in the undersampled signal. The resulting localization error is comparable with that of similar localization systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sensor%20swarms" title="sensor swarms">sensor swarms</a>, <a href="https://publications.waset.org/abstracts/search?q=localization" title=" localization"> localization</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20swarms" title=" wireless sensor swarms"> wireless sensor swarms</a>, <a href="https://publications.waset.org/abstracts/search?q=scalable%20energy" title=" scalable energy"> scalable energy</a> </p> <a href="https://publications.waset.org/abstracts/29900/influence-of-scalable-energy-related-sensor-parameters-on-acoustic-localization-accuracy-in-wireless-sensor-swarms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29900.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">422</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">4</span> Comparison of Direction of Arrival Estimation Method for Drone Based on Phased Microphone Array</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiwon%20Lee">Jiwon Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Yeong-Ju%20Go"> Yeong-Ju Go</a>, <a href="https://publications.waset.org/abstracts/search?q=Jong-Soo%20Choi"> Jong-Soo Choi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Drones were first developed for military use and were used in World War 1. But recently drones have been used in a variety of fields. Several companies actively utilize drone technology to strengthen their services, and in agriculture, drones are used for crop monitoring and sowing. Other people use drones for hobby activities such as photography. However, as the range of use of drones expands rapidly, problems caused by drones such as improperly flying, privacy and terrorism are also increasing. As the need for monitoring and tracking of drones increases, researches are progressing accordingly. The drone detection system estimates the position of the drone using the physical phenomena that occur when the drones fly. The drone detection system measures being developed utilize many approaches, such as radar, infrared camera, and acoustic detection systems. Among the various drone detection system, the acoustic detection system is advantageous in that the microphone array system is small, inexpensive, and easy to operate than other systems. In this paper, the acoustic signal is acquired by using minimum microphone when drone is flying, and direction of drone is estimated. When estimating the Direction of Arrival(DOA), there is a method of calculating the DOA based on the Time Difference of Arrival(TDOA) and a method of calculating the DOA based on the beamforming. The TDOA technique requires less number of microphones than the beamforming technique, but is weak in noisy environments and can only estimate the DOA of a single source. The beamforming technique requires more microphones than the TDOA technique. However, it is strong against the noisy environment and it is possible to simultaneously estimate the DOA of several drones. When estimating the DOA using acoustic signals emitted from the drone, it is impossible to measure the position of the drone, and only the direction can be estimated. To overcome this problem, in this work we show how to estimate the position of drones by arranging multiple microphone arrays. The microphone array used in the experiments was four tetrahedral microphones. We simulated the performance of each DOA algorithm and demonstrated the simulation results through experiments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acoustic%20sensing" title="acoustic sensing">acoustic sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=direction%20of%20arrival" title=" direction of arrival"> direction of arrival</a>, <a href="https://publications.waset.org/abstracts/search?q=drone%20detection" title=" drone detection"> drone detection</a>, <a href="https://publications.waset.org/abstracts/search?q=microphone%20array" title=" microphone array"> microphone array</a> </p> <a href="https://publications.waset.org/abstracts/94230/comparison-of-direction-of-arrival-estimation-method-for-drone-based-on-phased-microphone-array" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94230.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">160</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">3</span> Study on Acoustic Source Detection Performance Improvement of Microphone Array Installed on Drones Using Blind Source Separation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Youngsun%20Moon">Youngsun Moon</a>, <a href="https://publications.waset.org/abstracts/search?q=Yeong-Ju%20Go"> Yeong-Ju Go</a>, <a href="https://publications.waset.org/abstracts/search?q=Jong-Soo%20Choi"> Jong-Soo Choi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most drones that currently have surveillance/reconnaissance missions are basically equipped with optical equipment, but we also need to use a microphone array to estimate the location of the acoustic source. This can provide additional information in the absence of optical equipment. The purpose of this study is to estimate Direction of Arrival (DOA) based on Time Difference of Arrival (TDOA) estimation of the acoustic source in the drone. The problem is that it is impossible to measure the clear target acoustic source because of the drone noise. To overcome this problem is to separate the drone noise and the target acoustic source using Blind Source Separation(BSS) based on Independent Component Analysis(ICA). ICA can be performed assuming that the drone noise and target acoustic source are independent and each signal has non-gaussianity. For maximized non-gaussianity each signal, we use Negentropy and Kurtosis based on probability theory. As a result, we can improve TDOA estimation and DOA estimation of the target source in the noisy environment. We simulated the performance of the DOA algorithm applying BSS algorithm, and demonstrated the simulation through experiment at the anechoic wind tunnel. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aeroacoustics" title="aeroacoustics">aeroacoustics</a>, <a href="https://publications.waset.org/abstracts/search?q=acoustic%20source%20detection" title=" acoustic source detection"> acoustic source detection</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20difference%20of%20arrival" title=" time difference of arrival"> time difference of arrival</a>, <a href="https://publications.waset.org/abstracts/search?q=direction%20of%20arrival" title=" direction of arrival"> direction of arrival</a>, <a href="https://publications.waset.org/abstracts/search?q=blind%20source%20separation" title=" blind source separation"> blind source separation</a>, <a href="https://publications.waset.org/abstracts/search?q=independent%20component%20analysis" title=" independent component analysis"> independent component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=drone" title=" drone"> drone</a> </p> <a href="https://publications.waset.org/abstracts/94236/study-on-acoustic-source-detection-performance-improvement-of-microphone-array-installed-on-drones-using-blind-source-separation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94236.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">162</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">2</span> Received Signal Strength Indicator Based Localization of Bluetooth Devices Using Trilateration: An Improved Method for the Visually Impaired People</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Irfan%20Aziz">Muhammad Irfan Aziz</a>, <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Owens"> Thomas Owens</a>, <a href="https://publications.waset.org/abstracts/search?q=Uzair%20Khaleeq%20uz%20Zaman"> Uzair Khaleeq uz Zaman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The instantaneous and spatial localization for visually impaired people in dynamically changing environments with unexpected hazards and obstacles, is the most demanding and challenging issue faced by the navigation systems today. Since Bluetooth cannot utilize techniques like Time Difference of Arrival (TDOA) and Time of Arrival (TOA), it uses received signal strength indicator (RSSI) to measure Receive Signal Strength (RSS). The measurements using RSSI can be improved significantly by improving the existing methodologies related to RSSI. Therefore, the current paper focuses on proposing an improved method using trilateration for localization of Bluetooth devices for visually impaired people. To validate the method, class 2 Bluetooth devices were used along with the development of a software. Experiments were then conducted to obtain surface plots that showed the signal interferences and other environmental effects. Finally, the results obtained show the surface plots for all Bluetooth modules used along with the strong and weak points depicted as per the color codes in red, yellow and blue. It was concluded that the suggested improved method of measuring RSS using trilateration helped to not only measure signal strength affectively but also highlighted how the signal strength can be influenced by atmospheric conditions such as noise, reflections, etc. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bluetooth" title="Bluetooth">Bluetooth</a>, <a href="https://publications.waset.org/abstracts/search?q=indoor%2Foutdoor%20localization" title=" indoor/outdoor localization"> indoor/outdoor localization</a>, <a href="https://publications.waset.org/abstracts/search?q=received%20signal%20strength%20indicator" title=" received signal strength indicator"> received signal strength indicator</a>, <a href="https://publications.waset.org/abstracts/search?q=visually%20impaired" title=" visually impaired"> visually impaired</a> </p> <a href="https://publications.waset.org/abstracts/101797/received-signal-strength-indicator-based-localization-of-bluetooth-devices-using-trilateration-an-improved-method-for-the-visually-impaired-people" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101797.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">1</span> An Autonomous Passive Acoustic System for Detection, Tracking and Classification of Motorboats in Portofino Sea</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Casale">A. Casale</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Alessi"> J. Alessi</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20N.%20Bianchi"> C. N. Bianchi</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Bozzini"> G. Bozzini</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Brunoldi"> M. Brunoldi</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Cappanera"> V. Cappanera</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Corvisiero"> P. Corvisiero</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Fanciulli"> G. Fanciulli</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Grosso"> D. Grosso</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Magnoli"> N. Magnoli</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Mandich"> A. Mandich</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Melchiorre"> C. Melchiorre</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Morri"> C. Morri</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Povero"> P. Povero</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Stasi"> N. Stasi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Taiuti"> M. Taiuti</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Viano"> G. Viano</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Wurtz"> M. Wurtz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work describes a real-time algorithm for detecting, tracking and classifying single motorboats, developed using the acoustic data recorded by a hydrophone array within the framework of EU LIFE + project ARION (LIFE09NAT/IT/000190). The project aims to improve the conservation status of bottlenose dolphins through a real-time simultaneous monitoring of their population and surface ship traffic. A Passive Acoustic Monitoring (PAM) system is installed on two autonomous permanent marine buoys, located close to the boundaries of the Marine Protected Area (MPA) of Portofino (Ligurian Sea- Italy). Detecting surface ships is also a necessity in many other sensible areas, such as wind farms, oil platforms, and harbours. A PAM system could be an effective alternative to the usual monitoring systems, as radar or active sonar, for localizing unauthorized ship presence or illegal activities, with the advantage of not revealing its presence. Each ARION buoy consists of a particular type of structure, named meda elastica (elastic beacon) composed of a main pole, about 30-meter length, emerging for 7 meters, anchored to a mooring of 30 tons at 90 m depth by an anti-twist steel wire. Each buoy is equipped with a floating element and a hydrophone tetrahedron array, whose raw data are send via a Wi-Fi bridge to a ground station where real-time analysis is performed. Bottlenose dolphin detection algorithm and ship monitoring algorithm are operating in parallel and in real time. Three modules were developed and commissioned for ship monitoring. The first is the detection algorithm, based on Time Difference Of Arrival (TDOA) measurements, i.e., the evaluation of angular direction of the target respect to each buoy and the triangulation for obtaining the target position. The second is the tracking algorithm, based on a Kalman filter, i.e., the estimate of the real course and speed of the target through a predictor filter. At last, the classification algorithm is based on the DEMON method, i.e., the extraction of the acoustic signature of single vessels. The following results were obtained; the detection algorithm succeeded in evaluating the bearing angle with respect to each buoy and the position of the target, with an uncertainty of 2 degrees and a maximum range of 2.5 km. The tracking algorithm succeeded in reconstructing the real vessel courses and estimating the speed with an accuracy of 20% respect to the Automatic Identification System (AIS) signals. The classification algorithm succeeded in isolating the acoustic signature of single vessels, demonstrating its temporal stability and the consistency of both buoys results. As reference, the results were compared with the Hilbert transform of single channel signals. The algorithm for tracking multiple targets is ready to be developed, thanks to the modularity of the single ship algorithm: the classification module will enumerate and identify all targets present in the study area; for each of them, the detection module and the tracking module will be applied to monitor their course. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acoustic-noise" title="acoustic-noise">acoustic-noise</a>, <a href="https://publications.waset.org/abstracts/search?q=bottlenose-dolphin" title=" bottlenose-dolphin"> bottlenose-dolphin</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrophone" title=" hydrophone"> hydrophone</a>, <a href="https://publications.waset.org/abstracts/search?q=motorboat" title=" motorboat"> motorboat</a> </p> <a 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