Gaussian mixture PHD filter for multi-target tracking using passive doppler-only measurements

Date

2012-05-17

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

167
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 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 scenario. Simulation results show that the GM-PHD filter success fully tracks multiple targets using only Doppler shift measurements in a passive multi-static scenario.

Subject Keywords

Random sets, Multi-target tracking, Gaussian mixture probability hypothesis density filter, Doppler measurements

URI

https://hdl.handle.net/11511/46822

DOI

https://doi.org/10.1049/cp.2012.0417

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Multi-target tracking using passive doppler measurements Guldogan, Mehmet B.; Orguner, Umut; Gustafsson, Fredrik (2013-04-26) 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.
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...
Outlier robust filters and their multiple model extensions Şahin Bozgan, İlknur; Özkan, Emre; Department of Electrical and Electronics Engineering (2019) Kalman filter (KF), which is an algorithm that is utilized to estimate unknown variables based on noisy measurements, has been successfully employed in many applications such as navigation, control, signal processing and target tracking. It is the optimum Bayesian filter in terms of mean square error (MSE) for linear Gaussian state-space models (SSMs). However, in many real world applications, the performance of KF degrades due to the presence of outliers in noises. Motivated by this problem, several algori...
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 ...
Extended target tracking with a cardinalized probability hypothesis density filter Orguner, Umut; Granström, Karl (null; 2011-07-08) This paper presents a cardinalized probability hypothesis density (CPHD) filter for extended targets that can result in multiple measurements at each scan. The probability hypothesis density (PHD) filter for such targets has already been derived by Mahler and a Gaussian mixture implementation has been proposed recently. This work relaxes the Poisson assumptions of the extended target PHD filter in target and measurement numbers to achieve better estimation performance. A Gaussian mixture implementation is d...

Citation Formats

M. B. Guldogan, U. Orguner, and F. Gustafsson, “Gaussian mixture PHD filter for multi-target tracking using passive doppler-only measurements,” 2012, vol. 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/46822.