Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network
A new optimization algorithm of sensor selection is proposed in this paper for decentralized large-scale multi-target tracking (MTT) network within a labeled random finite set (RFS) framework. The method is performed based on a marginalized δ-generalized labeled multi-Bernoulli RFS. The rule of weig...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/18/12/4115 |
| _version_ | 1798040106624352256 |
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| author | Feng Lian Liming Hou Bo Wei Chongzhao Han |
| author_facet | Feng Lian Liming Hou Bo Wei Chongzhao Han |
| author_sort | Feng Lian |
| collection | DOAJ |
| description | A new optimization algorithm of sensor selection is proposed in this paper for decentralized large-scale multi-target tracking (MTT) network within a labeled random finite set (RFS) framework. The method is performed based on a marginalized δ-generalized labeled multi-Bernoulli RFS. The rule of weighted Kullback-Leibler average (KLA) is used to fuse local multi-target densities. A new metric, named as the label assignment (LA) metric, is proposed to measure the distance for two labeled sets. The lower bound of LA metric based mean square error between the labeled multi-target state set and its estimate is taken as the optimized objective function of sensor selection. The proposed bound is obtained by the information inequality to RFS measurement. Then, we present the sequential Monte Carlo and Gaussian mixture implementations for the bound. Another advantage of the bound is that it provides a basis for setting the weights of KLA. The coordinate descent method is proposed to compromise the computational cost of sensor selection and the accuracy of MTT. Simulations verify the effectiveness of our method under different signal-to- noise ratio scenarios. |
| first_indexed | 2024-04-11T22:02:56Z |
| format | Article |
| id | doaj.art-19c373c4d43d423096f75c7415776054 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:02:56Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-19c373c4d43d423096f75c74157760542022-12-22T04:00:51ZengMDPI AGSensors1424-82202018-11-011812411510.3390/s18124115s18124115Sensor Selection for Decentralized Large-Scale Multi-Target Tracking NetworkFeng Lian0Liming Hou1Bo Wei2Chongzhao Han3Ministry of Education Key Laboratory for Intelligent Networks and Network Security (MOE KLINNS), School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaMinistry of Education Key Laboratory for Intelligent Networks and Network Security (MOE KLINNS), School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaMinistry of Education Key Laboratory for Intelligent Networks and Network Security (MOE KLINNS), School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaMinistry of Education Key Laboratory for Intelligent Networks and Network Security (MOE KLINNS), School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaA new optimization algorithm of sensor selection is proposed in this paper for decentralized large-scale multi-target tracking (MTT) network within a labeled random finite set (RFS) framework. The method is performed based on a marginalized δ-generalized labeled multi-Bernoulli RFS. The rule of weighted Kullback-Leibler average (KLA) is used to fuse local multi-target densities. A new metric, named as the label assignment (LA) metric, is proposed to measure the distance for two labeled sets. The lower bound of LA metric based mean square error between the labeled multi-target state set and its estimate is taken as the optimized objective function of sensor selection. The proposed bound is obtained by the information inequality to RFS measurement. Then, we present the sequential Monte Carlo and Gaussian mixture implementations for the bound. Another advantage of the bound is that it provides a basis for setting the weights of KLA. The coordinate descent method is proposed to compromise the computational cost of sensor selection and the accuracy of MTT. Simulations verify the effectiveness of our method under different signal-to- noise ratio scenarios.https://www.mdpi.com/1424-8220/18/12/4115sensor selectionmulti-target trackinglabeled random finite setdecentralized sensor networkerror bound |
| spellingShingle | Feng Lian Liming Hou Bo Wei Chongzhao Han Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network Sensors sensor selection multi-target tracking labeled random finite set decentralized sensor network error bound |
| title | Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network |
| title_full | Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network |
| title_fullStr | Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network |
| title_full_unstemmed | Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network |
| title_short | Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network |
| title_sort | sensor selection for decentralized large scale multi target tracking network |
| topic | sensor selection multi-target tracking labeled random finite set decentralized sensor network error bound |
| url | https://www.mdpi.com/1424-8220/18/12/4115 |
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