Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering
AbstractIn 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
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Zhang, Q.; Song, T.L. Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering. Sensors 2016, 16, 1469.
Zhang Q, Song TL. Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering. Sensors. 2016; 16(9):1469.Chicago/Turabian Style
Zhang, Qian; Song, Taek L. 2016. "Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering." Sensors 16, no. 9: 1469.
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