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Article

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

Department of Electronic Systems Engineering, Hanyang University, Ansan, Gyeonggi-do 15588, Korea
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Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Sensors 2016, 16(9), 1469; https://doi.org/10.3390/s16091469
Received: 25 July 2016 / Revised: 5 September 2016 / Accepted: 7 September 2016 / Published: 10 September 2016
(This article belongs to the Section Physical Sensors)
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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MDPI and ACS Style

Zhang, Q.; Song, T.L. Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering. Sensors 2016, 16, 1469. https://doi.org/10.3390/s16091469

AMA Style

Zhang Q, Song TL. Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering. Sensors. 2016; 16(9):1469. https://doi.org/10.3390/s16091469

Chicago/Turabian Style

Zhang, Qian, and Taek Lyul Song. 2016. "Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering" Sensors 16, no. 9: 1469. https://doi.org/10.3390/s16091469

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