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Sensors 2016, 16(9), 1469; doi:10.3390/s16091469

Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering

Department of Electronic Systems Engineering, Hanyang University, Ansan, Gyeonggi-do 15588, Korea
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 25 July 2016 / Revised: 5 September 2016 / Accepted: 7 September 2016 / Published: 10 September 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [951 KB, uploaded 10 September 2016]   |  


In this paper, an improved nonlinear Gaussian mixture probability hypothesis density (GM-PHD) filter is proposed to address bearings-only measurements in multi-target tracking. The proposed method, called the Gaussian mixture measurements-probability hypothesis density (GMM-PHD) filter, not only approximates the posterior intensity using a Gaussian mixture, but also models the likelihood function with a Gaussian mixture instead of a single Gaussian distribution. Besides, the target birth model of the GMM-PHD filter is assumed to be partially uniform instead of a Gaussian mixture. Simulation results show that the proposed filter outperforms the GM-PHD filter embedded with the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). View Full-Text
Keywords: nonlinear estimation; bearings-only measurement; multi-target tracking; Gaussian mixture measurements; passive sensor nonlinear estimation; bearings-only measurement; multi-target tracking; Gaussian mixture measurements; passive sensor

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhang, Q.; Song, T.L. Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering. Sensors 2016, 16, 1469.

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