Multi-target tracking using passive doppler measurements

Date

2013-04-26

Author

Guldogan, Mehmet B.
Orguner, Umut
Gustafsson, Fredrik

Metadata

Show full item record

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

Item Usage Stats

298
views

0
downloads

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

Suggestions

OpenMETU
Core

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...
Multiple target tracking with Gaussian mixture PHD filter using passive acoustic Doppler-only measurements Guldogan, Mehmet B.; Lindgren, David; Gustafsson, Fredrik; Habberstad, Hans; Orguner, Umut (null; 2012-09-12) In this paper, we present the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple ground targets using a passive acoustic-sensor network. For this purpose, an experimental setup consisting of a network of microphones and a loudspeaker was prepared. Non-cooperative transmissions from a loudspeaker (i.e. illuminator of opportunity) are exploited by non-directional separately located microphones (i.e. Doppler measuring sensors). Experimental proof-of-concept ...
Background tracking of a video taken from a front camera of non maneuvering vehicle Ünver, Önder; Demirekler, Mübeccel; Department of Electrical and Electronics Engineering (2014) In this study, a novel background tracking technique is proposed that uses extended Kalman Gaussian mixture probability hypothesis density filtering approach. Since the background in a movie, taken from a front camera of a non maneuvering moving vehicle, exhibits a non-stationary nature, tracking the background is usually done by using pixel-wise comparisons in consequent frames. Besides, some methods use features of the background to track it. The proposed method uses the feature tracking approach. The fea...
Target tracking and sensor placement for doppler–only measurements Ayazgök, Süleyman; Orguner, Umut; Department of Electrical and Electronics Engineering (2015) This thesis investigates the problems of target tracking and optimal sensor placement with Doppler-only measurements. First, a single point track initialization algorithm proposed in the literature is investigated for Doppler-only tracking. The initialization algorithm is based on separable least squares method and involves a grid-based optimization. Second, particle filters are considered for Doppler-only tracking and they are compared to an extended Kalman filter (EKF). It is shown that a classical bootst...

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.