Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking
1
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
2
Key laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xi’an 710071, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(4), 980; https://doi.org/10.3390/s19040980
Received: 2 February 2019
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Revised: 20 February 2019
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Accepted: 21 February 2019
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Published: 25 February 2019
(This article belongs to the Special Issue Multiple Object Tracking: Making Sense of the Sensors)
This paper presents a novel multi-objective optimization based sensor selection method for multi-target tracking in sensor networks. The multi-target states are modelled as multi-Bernoulli random finite sets and the multi-Bernoulli filter is used to propagate the multi-target posterior density. The proposed method is designed to select the sensor that provides the most reliable cardinality estimate, since more accurate cardinality estimate indicates more accurate target states. In the multi-Bernoulli filter, the updated multi-target density is a multi-Bernoulli random finite set formed by a union of legacy tracks and measurement-updated tracks. The legacy track and the measurement-updated track have different theoretical and physical meanings, and hence these two kinds of tracks are considered separately in the sensor management problem. Specifically, two objectives are considered: (1) maximizing the mean cardinality of the measurement-updated tracks, (2) minimizing the cardinality variance of the legacy tracks. Considering the conflicting objectives simultaneously is a multi-objective optimization problem. Tradeoff solutions between two conflicting objectives will be derived. Theoretical analysis and examples show that the proposed approach is effective and direct. The performance of the proposed method is demonstrated using two scenarios with different levels of observability of targets in the passive sensor network.
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MDPI and ACS Style
Zhu, Y.; Wang, J.; Liang, S. Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking. Sensors 2019, 19, 980. https://doi.org/10.3390/s19040980
AMA Style
Zhu Y, Wang J, Liang S. Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking. Sensors. 2019; 19(4):980. https://doi.org/10.3390/s19040980
Chicago/Turabian StyleZhu, Yun, Jun Wang, and Shuang Liang. 2019. "Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking" Sensors 19, no. 4: 980. https://doi.org/10.3390/s19040980
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