Yazar "Orguner, Umut" seçeneğine göre listele

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    Multi-target tracking using passive doppler measurements
    (2013) Güldoğan, Mehmet Burak; Orguner, Umut; Gustafsson, Fredrik K.
    In this paper, we analyze the perjorznance oj the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple non-cooperative targets using Doppler-only measurements in a passive sensor network. Clutter, missed detections and multi-static Doppler variances are incorporated into a realistic multi-target scenario. Simulation results show that the GM-PHD filter successjully tracks multiple targets using only Doppler shift measurements in a passive multi-static scenario. © 2013 IEEE. © 2013 Elsevier B.V., All rights reserved.
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    MULTI-TARGET TRACKING USING PASSIVE DOPPLER MEASUREMENTS
    (Ieee, 2013) Guldogan, Mehmet B.; Orguner, Umut; Gustafsson, Fredrik
    In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple non-cooperative targets using Doppler-only measurements in a passive sensor network. Clutter, missed detections and multi-static Doppler variances are incorporated into a realistic multi-target scenario. Simulation results show that the GM-PHD filter successfully tracks multiple targets using only Doppler shift measurements in a passive multi-static scenario.
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    Multi-target tracking with PHD filter using Doppler-only measurements
    (Academic Press Inc Elsevier Science, 2014) Guldogan, Mehmet B.; Lindgren, David; Gustafsson, Fredrik; Habberstad, Hans; Orguner, Umut
    In this paper, we address the problem of multi-target detection and tracking over a network of separately located Doppler-shift measuring sensors. For this challenging problem, we propose to use the probability hypothesis density (PHD) filter and present two implementations of the PHD filter, namely the sequential Monte Carlo PHD (SMC-PHD) and the Gaussian mixture PHD (GM-PHD) filters. Performances of both filters are carefully studied and compared for the considered challenging tracking problem. Simulation results show that both PHD filter implementations successfully track multiple targets using only Doppler shift measurements. Moreover, as a proof-of-concept, an experimental setup consisting of a network of microphones and a loudspeaker was prepared. Experimental study results reveal that it is possible to track multiple ground targets using acoustic Doppler shift measurements in a passive multi-static scenario. We observed that the GM-PHD is more effective, efficient and easy to implement than the SMC-PHD filter. (C) 2014 Elsevier Inc. All rights reserved.