Robust IMM Filtering Approach with Adaptive Estimation of Measurement Loss Probability for Surface Target Tracking
A suitable jump Markov system (JMS) filtering approach provides an efficient technique for tracking surface targets. In complex surface target tracking situations, due to the joint influences of lost measurements with an unknown probability and heavy-tailed measurement noise (HTMN), the estimation a...
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| Format: | Article |
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MDPI AG
2023-06-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/11/6/1243 |
| _version_ | 1797594031997321216 |
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| author | Chen Chen Weidong Zhou Lina Gao |
| author_facet | Chen Chen Weidong Zhou Lina Gao |
| author_sort | Chen Chen |
| collection | DOAJ |
| description | A suitable jump Markov system (JMS) filtering approach provides an efficient technique for tracking surface targets. In complex surface target tracking situations, due to the joint influences of lost measurements with an unknown probability and heavy-tailed measurement noise (HTMN), the estimation accuracy of conventional interacting multiple model (IMM) methods may be seriously degraded. Aiming to address the filtering issues in JMSs with HTMNs and random measurement losses, this paper presents an IMM filtering approach with the adaptive estimation of unknown measurement loss probability. In this study, we assumed that the measurement noises obey student’s t-distributions and then proposed Bernoulli random variables (BRVs) to characterize the random measurement loss. Notably, by converting the two likelihood functions from the weighted sum form to exponential multiplication, we established hierarchical Gaussian state space models to directly utilize the variational inference method. The system state vectors, unknown distribution parameters, BRVs, and unknown measurement loss probabilities were estimated simultaneously according to the variational Bayesian inference in the IMM framework. The results of maneuvering target tracking simulations verified that the presented filtering approach demonstrated superior estimation accuracy compared to existing IMM filters. |
| first_indexed | 2024-03-11T02:16:54Z |
| format | Article |
| id | doaj.art-ed62e8db520a4cd49b831784d3f3ad40 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-11T02:16:54Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-ed62e8db520a4cd49b831784d3f3ad402023-11-18T11:07:51ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-06-01116124310.3390/jmse11061243Robust IMM Filtering Approach with Adaptive Estimation of Measurement Loss Probability for Surface Target TrackingChen Chen0Weidong Zhou1Lina Gao2Department of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaDepartment of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaDepartment of Measurement and Control Engineering, School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, ChinaA suitable jump Markov system (JMS) filtering approach provides an efficient technique for tracking surface targets. In complex surface target tracking situations, due to the joint influences of lost measurements with an unknown probability and heavy-tailed measurement noise (HTMN), the estimation accuracy of conventional interacting multiple model (IMM) methods may be seriously degraded. Aiming to address the filtering issues in JMSs with HTMNs and random measurement losses, this paper presents an IMM filtering approach with the adaptive estimation of unknown measurement loss probability. In this study, we assumed that the measurement noises obey student’s t-distributions and then proposed Bernoulli random variables (BRVs) to characterize the random measurement loss. Notably, by converting the two likelihood functions from the weighted sum form to exponential multiplication, we established hierarchical Gaussian state space models to directly utilize the variational inference method. The system state vectors, unknown distribution parameters, BRVs, and unknown measurement loss probabilities were estimated simultaneously according to the variational Bayesian inference in the IMM framework. The results of maneuvering target tracking simulations verified that the presented filtering approach demonstrated superior estimation accuracy compared to existing IMM filters.https://www.mdpi.com/2077-1312/11/6/1243variational Bayesianinteracting multiple modelrandom measurement losssurface target trackingheavy-tailed measurement noises |
| spellingShingle | Chen Chen Weidong Zhou Lina Gao Robust IMM Filtering Approach with Adaptive Estimation of Measurement Loss Probability for Surface Target Tracking Journal of Marine Science and Engineering variational Bayesian interacting multiple model random measurement loss surface target tracking heavy-tailed measurement noises |
| title | Robust IMM Filtering Approach with Adaptive Estimation of Measurement Loss Probability for Surface Target Tracking |
| title_full | Robust IMM Filtering Approach with Adaptive Estimation of Measurement Loss Probability for Surface Target Tracking |
| title_fullStr | Robust IMM Filtering Approach with Adaptive Estimation of Measurement Loss Probability for Surface Target Tracking |
| title_full_unstemmed | Robust IMM Filtering Approach with Adaptive Estimation of Measurement Loss Probability for Surface Target Tracking |
| title_short | Robust IMM Filtering Approach with Adaptive Estimation of Measurement Loss Probability for Surface Target Tracking |
| title_sort | robust imm filtering approach with adaptive estimation of measurement loss probability for surface target tracking |
| topic | variational Bayesian interacting multiple model random measurement loss surface target tracking heavy-tailed measurement noises |
| url | https://www.mdpi.com/2077-1312/11/6/1243 |
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