Abstract
In this paper, we study the multi-sensor multi-target tracking problem in the formulation of random finite sets. The Gaussian Mixture probability hypothesis density (GM-PHD) method is employed to formulate the sequential fusing multi-sensor GM-PHD (SFMGM-PHD) algorithm. First, the GM-PHD is applied to multiple sensors to get the posterior GM estimations in a parallel way. Second, we propose the SFMGM-PHD algorithm to fuse the multi-sensor GM estimations in a sequential way. Third, the unbalanced weighted fusing and adaptive sequence ordering methods are further proposed for two improved SFMGM-PHD algorithms. At last, we analyze the proposed algorithms in four different multi-sensor multi-target tracking scenes, and the results demonstrate the efficiency.
Highlights
The Multi-sensor Multi-target Tracking (MMT) technique generally refers to the process of estimating the targets’ number and dynamic states from multi-sensor observations
The main contributions of this paper are as follows: (1) We propose the SFMGM-Probability Hypothesis Density (PHD) algorithm which can fuse the multi-sensor information sequentially at the estimated posterior Gaussian Mixture (GM) layer
(3) Two improved SFMGM-PHD algorithms are proposed based on the unbalanced weighted fusing and adaptive sequence ordering methods
Summary
The Multi-sensor Multi-target Tracking (MMT) technique generally refers to the process of estimating the targets’ number and dynamic states from multi-sensor observations. For the RFS based MMT algorithms, there are two classes of methods for obtaining the trajectory estimates: (1) The multi-target state estimation (point estimation) can be achieved without a DA process in the first place. For the DA based MMT algorithms, we often solve the SMT problem to obtain track estimations in the first place. Pao proposed a method to optimize the fusing order for the multi-sensor PDA algorithm that the data from the high quality sensor should be fused later [32]. The main contributions of this paper are as follows: (1) We propose the SFMGM-PHD algorithm which can fuse the multi-sensor information sequentially at the estimated posterior Gaussian Mixture (GM) layer. (3) Two improved SFMGM-PHD algorithms are proposed based on the unbalanced weighted fusing and adaptive sequence ordering methods.
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