Extended Object Tracking Based on Gaussian Process in Non-Gaussian Noise Environment
Extended object tracking (EOT) is a prominent research area in high-resolution radar surveillance, ship tracking, and video tracking. However, EOT algorithms are susceptible to non-Gaussian noise from factors such as sensor performance and environmental conditions. To address this problem, the...
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| Language: | English |
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Instituto de Aeronáutica e Espaço (IAE)
2025-01-01
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| Series: | Journal of Aerospace Technology and Management |
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| Online Access: | https://jatm.com.br/jatm/article/view/1356 |
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| author | Lifan Sun Yongning Wang Dan Gao |
| author_facet | Lifan Sun Yongning Wang Dan Gao |
| author_sort | Lifan Sun |
| collection | DOAJ |
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Extended object tracking (EOT) is a prominent research area in high-resolution radar surveillance, ship tracking, and video tracking. However, EOT algorithms are susceptible to non-Gaussian noise from factors such as sensor performance and environmental conditions. To address this problem, the Gaussian process (GP)-based maximum correntropy criterion square root cubature Kalman filter algorithm (GP-MCC-SRCKF) for EOT in non-Gaussian noise environments is proposed in this paper. The proposed method utilizes a GP to model extended objects, thereby enhancing estimation accuracy. Furthermore, weighted least squares (WLS) and MCC are incorporated to construct a cost function. The proposed method considers high-order moments of estimation error and effectively handles outliers in non-Gaussian noise environments. MCC-SRCKF improves the accuracy of object state estimation in non-Gaussian noise environments while ensuring the positive definiteness and symmetry of the error covariance matrix. Finally, simulation experiments are conducted to demonstrate the effectiveness of the proposed method.
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| format | Article |
| id | doaj-art-23dc19bed8684f88bef3b18874aa9039 |
| institution | Kabale University |
| issn | 2175-9146 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Instituto de Aeronáutica e Espaço (IAE) |
| record_format | Article |
| series | Journal of Aerospace Technology and Management |
| spelling | doaj-art-23dc19bed8684f88bef3b18874aa90392025-01-13T01:46:51ZengInstituto de Aeronáutica e Espaço (IAE)Journal of Aerospace Technology and Management2175-91462025-01-0116Extended Object Tracking Based on Gaussian Process in Non-Gaussian Noise EnvironmentLifan Sun0Yongning Wang1Dan Gao2Henan University of Science and Technology – School of Information Engineering – Luoyang – China | Longmen Laboratory – Luoyang – China | Henan Academy of Sciences – Zhengzhou – China.Henan University of Science and Technology – School of Information Engineering – Luoyang – China.Henan University of Science and Technology – School of Information Engineering – Luoyang – China. Extended object tracking (EOT) is a prominent research area in high-resolution radar surveillance, ship tracking, and video tracking. However, EOT algorithms are susceptible to non-Gaussian noise from factors such as sensor performance and environmental conditions. To address this problem, the Gaussian process (GP)-based maximum correntropy criterion square root cubature Kalman filter algorithm (GP-MCC-SRCKF) for EOT in non-Gaussian noise environments is proposed in this paper. The proposed method utilizes a GP to model extended objects, thereby enhancing estimation accuracy. Furthermore, weighted least squares (WLS) and MCC are incorporated to construct a cost function. The proposed method considers high-order moments of estimation error and effectively handles outliers in non-Gaussian noise environments. MCC-SRCKF improves the accuracy of object state estimation in non-Gaussian noise environments while ensuring the positive definiteness and symmetry of the error covariance matrix. Finally, simulation experiments are conducted to demonstrate the effectiveness of the proposed method. https://jatm.com.br/jatm/article/view/1356Gaussian processExtended object trackingMaximum correntropy criterionNon-Gaussian noise |
| spellingShingle | Lifan Sun Yongning Wang Dan Gao Extended Object Tracking Based on Gaussian Process in Non-Gaussian Noise Environment Journal of Aerospace Technology and Management Gaussian process Extended object tracking Maximum correntropy criterion Non-Gaussian noise |
| title | Extended Object Tracking Based on Gaussian Process in Non-Gaussian Noise Environment |
| title_full | Extended Object Tracking Based on Gaussian Process in Non-Gaussian Noise Environment |
| title_fullStr | Extended Object Tracking Based on Gaussian Process in Non-Gaussian Noise Environment |
| title_full_unstemmed | Extended Object Tracking Based on Gaussian Process in Non-Gaussian Noise Environment |
| title_short | Extended Object Tracking Based on Gaussian Process in Non-Gaussian Noise Environment |
| title_sort | extended object tracking based on gaussian process in non gaussian noise environment |
| topic | Gaussian process Extended object tracking Maximum correntropy criterion Non-Gaussian noise |
| url | https://jatm.com.br/jatm/article/view/1356 |
| work_keys_str_mv | AT lifansun extendedobjecttrackingbasedongaussianprocessinnongaussiannoiseenvironment AT yongningwang extendedobjecttrackingbasedongaussianprocessinnongaussiannoiseenvironment AT dangao extendedobjecttrackingbasedongaussianprocessinnongaussiannoiseenvironment |