1. Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering.
- Author
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Qian Zhang and Taek Lyul Song
- Subjects
- *
GAUSSIAN mixture models , *ARTIFICIAL satellite tracking , *KALMAN filtering , *CONTROL theory (Engineering) , *STATISTICAL hypothesis testing - Abstract
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). [ABSTRACT FROM AUTHOR]
- Published
- 2016
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