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Sensors 2016, 16(10), 1666; doi:10.3390/s16101666

Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target Tracking

1
School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
2
Key Laboratory of Information Fusion Technology, Ministry of China, Xi’an 710072, China
3
Southwest China Research Institute of Electronic Equipment (SWIEE), Chengdu 610036, China
*
Author to whom correspondence should be addressed.
Academic Editor: Xue-Bo Jin
Received: 31 July 2016 / Revised: 30 September 2016 / Accepted: 3 October 2016 / Published: 11 October 2016
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)
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Abstract

In this paper, an adaptive collaborative Gaussian Mixture Probability Hypothesis Density (ACo-GMPHD) filter is proposed for multi-target tracking with automatic track extraction. Based on the evolutionary difference between the persistent targets and the birth targets, the measurements are adaptively partitioned into two parts, persistent and birth measurement sets, for updating the persistent and birth target Probability Hypothesis Density, respectively. Furthermore, the collaboration mechanism of multiple probability hypothesis density (PHDs) is established, where tracks can be automatically extracted. Simulation results reveal that the proposed filter yields considerable computational savings in processing requirements and significant improvement in tracking accuracy. View Full-Text
Keywords: multi-target tracking; multi-target state and track extraction; GMPHD filter multi-target tracking; multi-target state and track extraction; GMPHD filter
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Yang, F.; Wang, Y.; Chen, H.; Zhang, P.; Liang, Y. Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target Tracking. Sensors 2016, 16, 1666.

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