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Open AccessArticle

Multisensor Multi-Target Tracking Based on GM-PHD Using Out-Of-Sequence Measurements

1
State Key Laboratory of Industrial Control Technology, Hangzhou 310027, China
2
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(19), 4315; https://doi.org/10.3390/s19194315
Received: 30 August 2019 / Revised: 28 September 2019 / Accepted: 1 October 2019 / Published: 5 October 2019
(This article belongs to the Collection Multi-Sensor Information Fusion)
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. View Full-Text
Keywords: out-of-sequence; multi-target tracking; random finite set; gaussian mixture probability hypothesis density out-of-sequence; multi-target tracking; random finite set; gaussian mixture probability hypothesis density
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MDPI and ACS Style

Liu, M.; Huai, T.; Zheng, R.; Zhang, S. Multisensor Multi-Target Tracking Based on GM-PHD Using Out-Of-Sequence Measurements. Sensors 2019, 19, 4315.

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