Abstract
In this paper, we study the issue of out-of-sequence measurement (OOSM) in a multi-target scenario to improve tracking performance. The OOSM is very common in tracking systems, and it would result in performance degradation if we used it inappropriately. Thus, OOSM should be fully utilized as far as possible. To improve the performance of the tracking system and use OOSM sufficiently, firstly, the problem of OOSM is formulated. Then the classical B1 algorithm for OOSM problem of single target tracking is given. Next, the random finite set (RFS)-based Gaussian mixture probability hypothesis density (GM-PHD) is introduced. Consequently, we derived the equation for re-updating of posterior intensity with OOSM. Implementation of GM-PHD using OOSM is also given. Finally, several simulations are given, and results show that tracking performance of GM-PHD using OOSM is better than GM-PHD using in-sequence measurement (ISM), which can strongly demonstrate the effectiveness of our proposed algorithm.
Highlights
Multi-target tracking has been studied widely in recent years
Using out-of-sequence measurement (OOSM) is better than Gaussian mixture probability hypothesis density (GM-Probability hypothesis density (PHD)) using in-sequence measurement (ISM), which can strongly demonstrate the effectiveness of our proposed algorithm
Probability hypothesis density (PHD) filter is a multi-target tracking algorithm based on random finite set (RFS) that has emerged in recent years
Summary
Multi-target tracking has been studied widely in recent years. It has attracted a lot of attention and is a major research hotspot in the field of target tracking. Probability hypothesis density (PHD) filter is a multi-target tracking algorithm based on random finite set (RFS) that has emerged in recent years. It was first proposed by Mahler in the literature [8]. After the state and observation are modeled as RFS, a multi-target tracking problem can be formulated in Bayes filtering. Under the assumption of the linear Gaussian multi-target model, the analytical solution of GM-PHD can be obtained This filtering algorithm is adopted in this paper.
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