Abstract
The sequential Monte Carlo probability hypothesis density (SMC-PHD) filter has been demonstrated to be a favorable method for multi-target tracking. However, the time-varying target states need to be extracted from the particle approximation of the posterior PHD, which is difficult to implement due to the unknown relations between the large amount of particles and the PHD peaks representing potential target locations. To address this problem, a novel multi-target state extraction algorithm is proposed in this paper. By exploiting the information of measurements and particle likelihoods in the filtering stage, we propose a validation mechanism which aims at selecting effective measurements and particles corresponding to detected targets. Subsequently, the state estimates of the detected and undetected targets are performed separately: the former are obtained from the particle clusters directed by effective measurements, while the latter are obtained from the particles corresponding to undetected targets via clustering method. Simulation results demonstrate that the proposed method yields better estimation accuracy and reliability compared to existing methods.
Highlights
Multi-target tracking (MTT) has to deal with the detection and estimation problems of multiple targets in a cluttered environment [1]. Traditional solutions such as multiple hypothesis tracking (MHT) filter and joint probabilistic data association (JPDA) filter handle this problem through a divide-and-conquer approach that involves data association and filtering processes [2,3]
The probability hypothesis density (PHD) filter [4] which is derived via first-order moment approximation of the multi-target posterior density, and its implementations such as sequential Monte Carlo PHD
The implementation of the PHD filter generally resorts to some approximate methods, and we focus on the SMC implementation proposed in [7], referred to as the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter
Summary
Multi-target tracking (MTT) has to deal with the detection and estimation problems of multiple targets in a cluttered environment [1]. As an alternative to the association-based methods, the random finite sets (RFS) approach is an emerging technique to multi-target tracking (MTT), and the resulting optimal multi-target Bayes filter provides a rigorous theoretical basis for many novel multi-target filters [4,5,6]. The SMC method leads to a troublesome problem in extracting the estimates of target states from the given particle approximation of the PHD ( known as intensity function), and the accuracy of the estimated multi-target state directly determines the tracking performance of a multi-target filtering algorithm. The CLEAN method [18] was proposed to extract target states from the SMC-PHD filter Since this method only exploits the weight information of particles, the average performance of the CLEAN method is no better than the k-means clustering in general.
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