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).
Highlights
Bearings-only multi-target tracking (MTT) [1,2,3] with clutter and missed detections is a challenging nonlinear problem
To handle the measurement origin uncertainty problem in MTT with clutters, many techniques have been developed. Many of these methods belong to the following categories: joint probability data association (JPDA) [18], multiple hypothesis tracking (MHT) [19,20] and random finite set (RFS) [11]
The performances of all filters are compared with the average optimal subpattern assignment (OSPA) metric over 500 Monte Carlo runs
Summary
Bearings-only multi-target tracking (MTT) [1,2,3] with clutter and missed detections is a challenging nonlinear problem. To handle the measurement origin uncertainty problem in MTT with clutters, many techniques have been developed Many of these methods belong to the following categories: joint probability data association (JPDA) [18], multiple hypothesis tracking (MHT) [19,20] and random finite set (RFS) [11]. Among RFS-based filters, the probability hypothesis density (PHD) [26] filter is a first moment approximation to the multi-target predicted and posterior densities It propagates the target intensities without considering data associations between targets and measurements. The Gaussian mixture measurements-PHD (GMM-PHD) filter is proposed to address bearings-only MTT with clutter and missed detections.
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