A Novel Robust IMM Filtering Method for Surface-Maneuvering Target Tracking with Random Measurement Delay
A proper filtering method for jump Markov system (JMS) is an effective approach for tracking a maneuvering target. Since the coexisting of heavy-tailed measurement noises (HTMNs) and one-step random measurement delay (OSRMD) in the complex scenarios of the surface maneuvering target tracking, the ef...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/11/5/1047 |
| _version_ | 1797599516428337152 |
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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 proper filtering method for jump Markov system (JMS) is an effective approach for tracking a maneuvering target. Since the coexisting of heavy-tailed measurement noises (HTMNs) and one-step random measurement delay (OSRMD) in the complex scenarios of the surface maneuvering target tracking, the effectiveness of typical interacting multiple model (IMM) techniques may decline severely. To solve the state estimation problem in JMSs with HTMN and OSRMD simultaneously, this article designs a novel robust IMM filter utilizing the variational Bayesian (VB) inference framework. This algorithm models the HTMNs as student’s t-distribuitons, and presents a random Bernoulli variable to describe the OSRMD in JMSs. By transforming measurement likelihood function form from weighted summation to exponential product, this paper constructs hierarchical Gaussian state space models. Then, the state vectors, random Bernoulli vairable, and model probability are inferred jointly according to VB inference. The surface maneuvering target tracking simulation example result indicates that the presented IMM filter achieves superior target state estimation accuracy among existing IMM filters. |
| first_indexed | 2024-03-11T03:35:35Z |
| format | Article |
| id | doaj.art-83683478d1cd4390868fc2ee6901c10c |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-11T03:35:35Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-83683478d1cd4390868fc2ee6901c10c2023-11-18T02:00:34ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-05-01115104710.3390/jmse11051047A Novel Robust IMM Filtering Method for Surface-Maneuvering Target Tracking with Random Measurement DelayChen 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 proper filtering method for jump Markov system (JMS) is an effective approach for tracking a maneuvering target. Since the coexisting of heavy-tailed measurement noises (HTMNs) and one-step random measurement delay (OSRMD) in the complex scenarios of the surface maneuvering target tracking, the effectiveness of typical interacting multiple model (IMM) techniques may decline severely. To solve the state estimation problem in JMSs with HTMN and OSRMD simultaneously, this article designs a novel robust IMM filter utilizing the variational Bayesian (VB) inference framework. This algorithm models the HTMNs as student’s t-distribuitons, and presents a random Bernoulli variable to describe the OSRMD in JMSs. By transforming measurement likelihood function form from weighted summation to exponential product, this paper constructs hierarchical Gaussian state space models. Then, the state vectors, random Bernoulli vairable, and model probability are inferred jointly according to VB inference. The surface maneuvering target tracking simulation example result indicates that the presented IMM filter achieves superior target state estimation accuracy among existing IMM filters.https://www.mdpi.com/2077-1312/11/5/1047variational Bayesiansurface maneuvering target trackingrandom measurement delayheavy-tailed measurement noiseinteracting multiple model |
| spellingShingle | Chen Chen Weidong Zhou Lina Gao A Novel Robust IMM Filtering Method for Surface-Maneuvering Target Tracking with Random Measurement Delay Journal of Marine Science and Engineering variational Bayesian surface maneuvering target tracking random measurement delay heavy-tailed measurement noise interacting multiple model |
| title | A Novel Robust IMM Filtering Method for Surface-Maneuvering Target Tracking with Random Measurement Delay |
| title_full | A Novel Robust IMM Filtering Method for Surface-Maneuvering Target Tracking with Random Measurement Delay |
| title_fullStr | A Novel Robust IMM Filtering Method for Surface-Maneuvering Target Tracking with Random Measurement Delay |
| title_full_unstemmed | A Novel Robust IMM Filtering Method for Surface-Maneuvering Target Tracking with Random Measurement Delay |
| title_short | A Novel Robust IMM Filtering Method for Surface-Maneuvering Target Tracking with Random Measurement Delay |
| title_sort | novel robust imm filtering method for surface maneuvering target tracking with random measurement delay |
| topic | variational Bayesian surface maneuvering target tracking random measurement delay heavy-tailed measurement noise interacting multiple model |
| url | https://www.mdpi.com/2077-1312/11/5/1047 |
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