Reduced‐order multisensory fusion estimation with application to object tracking
Abstract This paper investigates the track‐to‐track state estimation for a class of linear time‐varying multisensory systems. We propose a novel low‐complexity reduced‐order filter (ROF) under the Kalman filtering framework. Unlike the majority of previous track‐to‐track strategies, the proposed fus...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-IET
2022-06-01
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| Series: | IET Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/sil2.12120 |
| _version_ | 1797422346717364224 |
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| author | Vladimir Shin Vahid Hamdipoor Yoonsoo Kim |
| author_facet | Vladimir Shin Vahid Hamdipoor Yoonsoo Kim |
| author_sort | Vladimir Shin |
| collection | DOAJ |
| description | Abstract This paper investigates the track‐to‐track state estimation for a class of linear time‐varying multisensory systems. We propose a novel low‐complexity reduced‐order filter (ROF) under the Kalman filtering framework. Unlike the majority of previous track‐to‐track strategies, the proposed fusion strategy applies only to special variables or required components that contain critical information about a target system of interest. Also, unlike existing suboptimal fusion filters such as the covariance intersection, the proposed ROF algorithm makes use of nonzero cross‐covariances between local filters that greatly improve its estimation accuracy. The theoretical aspect of ROF application to multisensory systems with identical sensors is also thoroughly investigated. Finally, we show the effectiveness and accuracy of the ROF when applied to objects (including a drone) performing a two‐dimensional maneuver using numerical simulations. |
| first_indexed | 2024-03-09T07:30:59Z |
| format | Article |
| id | doaj.art-1dd64a031c6d4350a515366aa0b847b8 |
| institution | Directory Open Access Journal |
| issn | 1751-9675 1751-9683 |
| language | English |
| last_indexed | 2024-03-09T07:30:59Z |
| publishDate | 2022-06-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj.art-1dd64a031c6d4350a515366aa0b847b82023-12-03T06:12:52ZengHindawi-IETIET Signal Processing1751-96751751-96832022-06-0116446347810.1049/sil2.12120Reduced‐order multisensory fusion estimation with application to object trackingVladimir Shin0Vahid Hamdipoor1Yoonsoo Kim2Graduate School of Mechanical and Aerospace Engineering Gyeongsang National University Jinju KoreaDepartment of Electrical Engineering Qatar University Doha QatarGraduate School of Mechanical and Aerospace Engineering Gyeongsang National University Jinju KoreaAbstract This paper investigates the track‐to‐track state estimation for a class of linear time‐varying multisensory systems. We propose a novel low‐complexity reduced‐order filter (ROF) under the Kalman filtering framework. Unlike the majority of previous track‐to‐track strategies, the proposed fusion strategy applies only to special variables or required components that contain critical information about a target system of interest. Also, unlike existing suboptimal fusion filters such as the covariance intersection, the proposed ROF algorithm makes use of nonzero cross‐covariances between local filters that greatly improve its estimation accuracy. The theoretical aspect of ROF application to multisensory systems with identical sensors is also thoroughly investigated. Finally, we show the effectiveness and accuracy of the ROF when applied to objects (including a drone) performing a two‐dimensional maneuver using numerical simulations.https://doi.org/10.1049/sil2.12120covariance intersectionKalman filteringmultisensory systemreduced‐order filterstate estimationtrack‐to‐track fusion |
| spellingShingle | Vladimir Shin Vahid Hamdipoor Yoonsoo Kim Reduced‐order multisensory fusion estimation with application to object tracking IET Signal Processing covariance intersection Kalman filtering multisensory system reduced‐order filter state estimation track‐to‐track fusion |
| title | Reduced‐order multisensory fusion estimation with application to object tracking |
| title_full | Reduced‐order multisensory fusion estimation with application to object tracking |
| title_fullStr | Reduced‐order multisensory fusion estimation with application to object tracking |
| title_full_unstemmed | Reduced‐order multisensory fusion estimation with application to object tracking |
| title_short | Reduced‐order multisensory fusion estimation with application to object tracking |
| title_sort | reduced order multisensory fusion estimation with application to object tracking |
| topic | covariance intersection Kalman filtering multisensory system reduced‐order filter state estimation track‐to‐track fusion |
| url | https://doi.org/10.1049/sil2.12120 |
| work_keys_str_mv | AT vladimirshin reducedordermultisensoryfusionestimationwithapplicationtoobjecttracking AT vahidhamdipoor reducedordermultisensoryfusionestimationwithapplicationtoobjecttracking AT yoonsookim reducedordermultisensoryfusionestimationwithapplicationtoobjecttracking |