Multi-target tracking using passive doppler measurements

Date

2013-04-26

Author

Guldogan, Mehmet B.
Orguner, Umut
Gustafsson, Fredrik

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Observability, Doppler radar, Doppler shift, Gaussian processes, Passive networks, Probability, Target tracking

URI

https://hdl.handle.net/11511/46499

DOI

https://doi.org/10.1109/siu.2013.6531165

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Gaussian mixture PHD filter for multi-target tracking using passive doppler-only measurements Guldogan, Mehmet B.; Orguner, Umut; Gustafsson, Fredrik (2012-05-17) In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple non-cooperative targets using a passive sensor network. Non-cooperative transmissions from illuminators of opportunity like GSM base stations, FM radio transmitters or digital broadcasters are exploited by non-directional separately located Doppler measuring sensors. Clutter, missed detections and multi-static Doppler variances are incorporated into a realistic multi-target s...
Multi-target tracking with PHD filter using Doppler-only measurements Guldogan, Mehmet B.; Lindgren, David; Gustafsson, Fredrik; Habberstad, Hans; Orguner, Umut (2014-04-01) 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...
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Citation Formats

M. B. Guldogan, U. Orguner, and F. Gustafsson, “Multi-target tracking using passive doppler measurements ,” 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/46499.