Adaptive Interacting Multiple Model Algorithm Based on Information-Weighted Consensus for Maneuvering Target Tracking
Networked multiple sensors are used to solve the problem of maneuvering target tracking. To avoid the linearization of nonlinear dynamic functions, and to obtain more accurate estimates for maneuvering targets, a novel adaptive information-weighted consensus filter for maneuvering target tracking is...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/18/7/2012 |
| _version_ | 1818040452541054976 |
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| author | Ziran Ding Yu Liu Jun Liu Kaimin Yu Yuanyang You Peiliang Jing You He |
| author_facet | Ziran Ding Yu Liu Jun Liu Kaimin Yu Yuanyang You Peiliang Jing You He |
| author_sort | Ziran Ding |
| collection | DOAJ |
| description | Networked multiple sensors are used to solve the problem of maneuvering target tracking. To avoid the linearization of nonlinear dynamic functions, and to obtain more accurate estimates for maneuvering targets, a novel adaptive information-weighted consensus filter for maneuvering target tracking is proposed. The pseudo measurement matrix is computed with unscented transform to utilize the information form of measurements, which is necessary for consensus iterations. To improve the maneuvering target tracking accuracy and get a unified estimation in each sensor node across the entire network, the adaptive current statistical model is exploited to update the estimate, and the information-weighted consensus protocol is applied among neighboring nodes for each dynamic model. Based on posterior probabilities of multiple models, the final estimate of each sensor is acquired with weighted combination of model-conditioned estimates. Experimental results illustrate the superior performance of the proposed algorithm with respect tracking accuracy and agreement of estimates in the whole network. |
| first_indexed | 2024-12-10T08:14:45Z |
| format | Article |
| id | doaj.art-5b81c81afe6a452eb4f6034ab9355d8e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-12-10T08:14:45Z |
| publishDate | 2018-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5b81c81afe6a452eb4f6034ab9355d8e2022-12-22T01:56:29ZengMDPI AGSensors1424-82202018-06-01187201210.3390/s18072012s18072012Adaptive Interacting Multiple Model Algorithm Based on Information-Weighted Consensus for Maneuvering Target TrackingZiran Ding0Yu Liu1Jun Liu2Kaimin Yu3Yuanyang You4Peiliang Jing5You He6Research Institute of Information Fusion, Naval Aviation University, Yantai 264001, ChinaResearch Institute of Information Fusion, Naval Aviation University, Yantai 264001, ChinaResearch Institute of Information Fusion, Naval Aviation University, Yantai 264001, ChinaThe First Training Base, Naval Aviation University, Huludao 125001, ChinaThe Second Training Base, Naval Aviation University, Changzhi 046000, ChinaChina Ordnance Test Center, Huayin 714200, ChinaResearch Institute of Information Fusion, Naval Aviation University, Yantai 264001, ChinaNetworked multiple sensors are used to solve the problem of maneuvering target tracking. To avoid the linearization of nonlinear dynamic functions, and to obtain more accurate estimates for maneuvering targets, a novel adaptive information-weighted consensus filter for maneuvering target tracking is proposed. The pseudo measurement matrix is computed with unscented transform to utilize the information form of measurements, which is necessary for consensus iterations. To improve the maneuvering target tracking accuracy and get a unified estimation in each sensor node across the entire network, the adaptive current statistical model is exploited to update the estimate, and the information-weighted consensus protocol is applied among neighboring nodes for each dynamic model. Based on posterior probabilities of multiple models, the final estimate of each sensor is acquired with weighted combination of model-conditioned estimates. Experimental results illustrate the superior performance of the proposed algorithm with respect tracking accuracy and agreement of estimates in the whole network.http://www.mdpi.com/1424-8220/18/7/2012state estimationmaneuvering target trackingmultiple sensor fusionconsensusinteracting multiple model |
| spellingShingle | Ziran Ding Yu Liu Jun Liu Kaimin Yu Yuanyang You Peiliang Jing You He Adaptive Interacting Multiple Model Algorithm Based on Information-Weighted Consensus for Maneuvering Target Tracking Sensors state estimation maneuvering target tracking multiple sensor fusion consensus interacting multiple model |
| title | Adaptive Interacting Multiple Model Algorithm Based on Information-Weighted Consensus for Maneuvering Target Tracking |
| title_full | Adaptive Interacting Multiple Model Algorithm Based on Information-Weighted Consensus for Maneuvering Target Tracking |
| title_fullStr | Adaptive Interacting Multiple Model Algorithm Based on Information-Weighted Consensus for Maneuvering Target Tracking |
| title_full_unstemmed | Adaptive Interacting Multiple Model Algorithm Based on Information-Weighted Consensus for Maneuvering Target Tracking |
| title_short | Adaptive Interacting Multiple Model Algorithm Based on Information-Weighted Consensus for Maneuvering Target Tracking |
| title_sort | adaptive interacting multiple model algorithm based on information weighted consensus for maneuvering target tracking |
| topic | state estimation maneuvering target tracking multiple sensor fusion consensus interacting multiple model |
| url | http://www.mdpi.com/1424-8220/18/7/2012 |
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