Yazar "Gustafsson, Fredrik" seçeneğine göre listele

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    Acoustic Source Localization in a Network of Doppler Shift Sensors
    (Ieee, 2013) Lindgren, David; Guldogan, Mehmet B.; Gustafsson, Fredrik; Habberstad, Hans; Hendeby, Gustaf
    It is well-known that the motion of an acoustic source can be estimated from Doppler shift observations. It is however not obvious how to design a sensor network to efficiently deliver the localization service. In this work a rather simplistic motion model is proposed that is aimed at sensor networks with realistic numbers of sensor nodes. It is also described how to efficiently solve the associated least squares optimization problem by Gauss-Newton variable projection techniques, and how to initiate the numerical search from simple features extracted from the observed frequency series. The methods are demonstrated on real data by determining the distance to a passing propeller-driven aircraft and by localizing an all-terrain vehicle. It is concluded that the processing components included are fairly mature for practical implementations in sensor networks.
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    Knowledge Exploitation for Human Micro-Doppler Classification
    (Ieee-Inst Electrical Electronics Engineers Inc, 2015) Karabacak, Cesur; Gurbuz, Sevgi Z.; Gurbuz, Ali C.; Guldogan, Mehmet B.; Hendeby, Gustaf; Gustafsson, Fredrik
    Micro-Doppler radar signatures have great potential for classifying pedestrians and animals, as well as their motion pattern, in a variety of surveillance applications. Due to the many degrees of freedom involved, real data need to be complemented with accurate simulated radar data to be able to successfully design and test radar signal processing algorithms. In many cases, the ability to collect real data is limited by monetary and practical considerations, whereas in a simulated environment, any desired scenario may be generated. Motion capture (MOCAP) has been used in several works to simulate the human micro-Doppler signature measured by radar; however, validation of the approach has only been done based on visual comparisons of micro-Doppler signatures. This work validates and, more importantly, extends the exploitation of MOCAP data not just to simulate micro-Doppler signatures but also to use the simulated signatures as a source of a priori knowledge to improve the classification performance of real radar data, particularly in the case when the total amount of data is small.
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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.