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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Instituto de Aeronáutica e Espaço (IAE)
2025-01-01
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| Series: | Journal of Aerospace Technology and Management |
| Subjects: | |
| Online Access: | https://jatm.com.br/jatm/article/view/1356 |
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| Summary: | 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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| ISSN: | 2175-9146 |