Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking
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...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-02-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/19/4/980 |
id |
doaj-44ec22e38f604085b1b48fb0a2c46b1c |
---|---|
record_format |
Article |
spelling |
doaj-44ec22e38f604085b1b48fb0a2c46b1c2020-11-25T01:01:11ZengMDPI AGSensors1424-82202019-02-0119498010.3390/s19040980s19040980Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target TrackingYun Zhu0Jun Wang1Shuang Liang2National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaKey laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xi’an 710071, ChinaThis 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.https://www.mdpi.com/1424-8220/19/4/980multi-target trackingsensor managementrandom finite setmulti-objective optimization |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yun Zhu Jun Wang Shuang Liang |
spellingShingle |
Yun Zhu Jun Wang Shuang Liang Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking Sensors multi-target tracking sensor management random finite set multi-objective optimization |
author_facet |
Yun Zhu Jun Wang Shuang Liang |
author_sort |
Yun Zhu |
title |
Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking |
title_short |
Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking |
title_full |
Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking |
title_fullStr |
Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking |
title_full_unstemmed |
Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking |
title_sort |
multi-objective optimization based multi-bernoulli sensor selection for multi-target tracking |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2019-02-01 |
description |
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. |
topic |
multi-target tracking sensor management random finite set multi-objective optimization |
url |
https://www.mdpi.com/1424-8220/19/4/980 |
work_keys_str_mv |
AT yunzhu multiobjectiveoptimizationbasedmultibernoullisensorselectionformultitargettracking AT junwang multiobjectiveoptimizationbasedmultibernoullisensorselectionformultitargettracking AT shuangliang multiobjectiveoptimizationbasedmultibernoullisensorselectionformultitargettracking |
_version_ |
1725210353849073664 |