Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering.
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).
Department of Electronic Systems Engineering, Hanyang University, Ansan, Gyeonggi-do 15588, Korea. zq2013750509@hanyang.ac.kr.
Qian
Q
Zhang
Department of Electronic Systems Engineering, Hanyang University, Ansan, Gyeonggi-do 15588, Korea. tsong@hanyang.ac.kr.
Taek Lyul
TL
Song
Gaussian mixture measurements; bearings-only measurement; multi-target tracking; nonlinear estimation; passive sensor