A Novel Adaptive Robust Cubature Kalman Filter for Maneuvering Target Tracking with Model Uncertainty and Abnormal Measurement Noises
The features of measurement and process noise are directly related to the optimal performance of the cubature Kalman filter. The maneuvering target model’s high level of uncertainty and non-Gaussian mean noise are typical issues that the radar tracking system must deal with, making it impossible to...
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MDPI AG
2023-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/15/6966 |
| _version_ | 1827730776811110400 |
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| author | Xiangzhou Ye Jian Wang Dongjie Wu Yong Zhang Bing Li |
| author_facet | Xiangzhou Ye Jian Wang Dongjie Wu Yong Zhang Bing Li |
| author_sort | Xiangzhou Ye |
| collection | DOAJ |
| description | The features of measurement and process noise are directly related to the optimal performance of the cubature Kalman filter. The maneuvering target model’s high level of uncertainty and non-Gaussian mean noise are typical issues that the radar tracking system must deal with, making it impossible to obtain the appropriate estimation. How to strike a compromise between high robustness and estimation accuracy while designing filters has always been challenging. The H-infinity filter is a widely used robust algorithm. Based on the H-infinity cubature Kalman filter (HCKF), a novel adaptive robust cubature Kalman filter (ARCKF) is suggested in this paper. There are two adaptable components in the algorithm. First, an adaptive fading factor addresses the model uncertainty issue brought on by the target’s maneuvering turn. Second, an improved Sage–Husa estimation based on the Mahalanobis distance (MD) is suggested to estimate the measurement noise covariance matrix adaptively. The new approach significantly increases the robustness and estimation precision of the HCKF. According to the simulation results, the suggested algorithm is more effective than the conventional HCKF at handling system model errors and abnormal observations. |
| first_indexed | 2024-03-11T00:16:27Z |
| format | Article |
| id | doaj.art-5ef2d8fedf0a4fbe9a071d1be4101f90 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:16:27Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5ef2d8fedf0a4fbe9a071d1be4101f902023-11-18T23:36:52ZengMDPI AGSensors1424-82202023-08-012315696610.3390/s23156966A Novel Adaptive Robust Cubature Kalman Filter for Maneuvering Target Tracking with Model Uncertainty and Abnormal Measurement NoisesXiangzhou Ye0Jian Wang1Dongjie Wu2Yong Zhang3Bing Li4Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, ChinaKey Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, ChinaKey Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, ChinaKey Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, ChinaKey Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, ChinaThe features of measurement and process noise are directly related to the optimal performance of the cubature Kalman filter. The maneuvering target model’s high level of uncertainty and non-Gaussian mean noise are typical issues that the radar tracking system must deal with, making it impossible to obtain the appropriate estimation. How to strike a compromise between high robustness and estimation accuracy while designing filters has always been challenging. The H-infinity filter is a widely used robust algorithm. Based on the H-infinity cubature Kalman filter (HCKF), a novel adaptive robust cubature Kalman filter (ARCKF) is suggested in this paper. There are two adaptable components in the algorithm. First, an adaptive fading factor addresses the model uncertainty issue brought on by the target’s maneuvering turn. Second, an improved Sage–Husa estimation based on the Mahalanobis distance (MD) is suggested to estimate the measurement noise covariance matrix adaptively. The new approach significantly increases the robustness and estimation precision of the HCKF. According to the simulation results, the suggested algorithm is more effective than the conventional HCKF at handling system model errors and abnormal observations.https://www.mdpi.com/1424-8220/23/15/6966target trackingH-infinity cubature Kalman filteradaptive fading factorSage–Husa |
| spellingShingle | Xiangzhou Ye Jian Wang Dongjie Wu Yong Zhang Bing Li A Novel Adaptive Robust Cubature Kalman Filter for Maneuvering Target Tracking with Model Uncertainty and Abnormal Measurement Noises Sensors target tracking H-infinity cubature Kalman filter adaptive fading factor Sage–Husa |
| title | A Novel Adaptive Robust Cubature Kalman Filter for Maneuvering Target Tracking with Model Uncertainty and Abnormal Measurement Noises |
| title_full | A Novel Adaptive Robust Cubature Kalman Filter for Maneuvering Target Tracking with Model Uncertainty and Abnormal Measurement Noises |
| title_fullStr | A Novel Adaptive Robust Cubature Kalman Filter for Maneuvering Target Tracking with Model Uncertainty and Abnormal Measurement Noises |
| title_full_unstemmed | A Novel Adaptive Robust Cubature Kalman Filter for Maneuvering Target Tracking with Model Uncertainty and Abnormal Measurement Noises |
| title_short | A Novel Adaptive Robust Cubature Kalman Filter for Maneuvering Target Tracking with Model Uncertainty and Abnormal Measurement Noises |
| title_sort | novel adaptive robust cubature kalman filter for maneuvering target tracking with model uncertainty and abnormal measurement noises |
| topic | target tracking H-infinity cubature Kalman filter adaptive fading factor Sage–Husa |
| url | https://www.mdpi.com/1424-8220/23/15/6966 |
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