Multi-target tracking with PHD filter using Doppler-only measurements

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Guldogan M. B., Lindgren D., Gustafsson F., Habberstad H., ORGUNER U.

DIGITAL SIGNAL PROCESSING, cilt.27, ss.1-11, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 27
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1016/j.dsp.2014.01.009
  • Dergi Adı: DIGITAL SIGNAL PROCESSING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1-11
  • Anahtar Kelimeler: Random sets, Multi-target tracking, Probability hypothesis density filter, Doppler measurements, Gaussian mixture, Sequential Monte Carlo, HYPOTHESIS DENSITY FILTER, INSTANTANEOUS FREQUENCY, TARGET TRACKING, BISTATIC RADAR, LOCALIZATION, SHIFTS, OBSERVABILITY, SYSTEMS, SIGNAL, SETS
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.