{"title":"基于m估计的三维AoA目标跟踪鲁棒卡尔曼滤波算法","authors":"Yuexin Zhao, Wangdong Qi, Peng Liu, Jie Lin","doi":"10.1109/ICICSP50920.2020.9231976","DOIUrl":null,"url":null,"abstract":"An attractive issue in numerous applications is to track targets in three-dimensional (3-D) space with angle of arrival (AoA) measurements through nonlinear filters. The tracking performance degradation caused by outlier prompts a variety of robust filters. In this paper, an M-estimation-based robust bias compensation Kalman filter algorithm (MR-BCKF) is proposed. This algorithm recasts the AoA measurement equation to a linear form by pseudo-linearization, and then incorporates the M-estimation criterion into pseudo linear Kalman filter to enhance robustness, followed by the bias compensation to improve tracking accuracy. An improved three-segment weight function based on Mahalanobis distance is established to handle outliers for each element, which does not require the noise characteristics. Simulation demonstrates that MR-BCKF has enhanced robustness against outliers at different levels and achieves more accurate tracking compared with other robust filters.","PeriodicalId":117760,"journal":{"name":"2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"M-Estimation-Based Robust Kalman Filter Algorithm for Three-Dimensional AoA Target Tracking\",\"authors\":\"Yuexin Zhao, Wangdong Qi, Peng Liu, Jie Lin\",\"doi\":\"10.1109/ICICSP50920.2020.9231976\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An attractive issue in numerous applications is to track targets in three-dimensional (3-D) space with angle of arrival (AoA) measurements through nonlinear filters. The tracking performance degradation caused by outlier prompts a variety of robust filters. In this paper, an M-estimation-based robust bias compensation Kalman filter algorithm (MR-BCKF) is proposed. This algorithm recasts the AoA measurement equation to a linear form by pseudo-linearization, and then incorporates the M-estimation criterion into pseudo linear Kalman filter to enhance robustness, followed by the bias compensation to improve tracking accuracy. An improved three-segment weight function based on Mahalanobis distance is established to handle outliers for each element, which does not require the noise characteristics. Simulation demonstrates that MR-BCKF has enhanced robustness against outliers at different levels and achieves more accurate tracking compared with other robust filters.\",\"PeriodicalId\":117760,\"journal\":{\"name\":\"2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICSP50920.2020.9231976\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICSP50920.2020.9231976","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
M-Estimation-Based Robust Kalman Filter Algorithm for Three-Dimensional AoA Target Tracking
An attractive issue in numerous applications is to track targets in three-dimensional (3-D) space with angle of arrival (AoA) measurements through nonlinear filters. The tracking performance degradation caused by outlier prompts a variety of robust filters. In this paper, an M-estimation-based robust bias compensation Kalman filter algorithm (MR-BCKF) is proposed. This algorithm recasts the AoA measurement equation to a linear form by pseudo-linearization, and then incorporates the M-estimation criterion into pseudo linear Kalman filter to enhance robustness, followed by the bias compensation to improve tracking accuracy. An improved three-segment weight function based on Mahalanobis distance is established to handle outliers for each element, which does not require the noise characteristics. Simulation demonstrates that MR-BCKF has enhanced robustness against outliers at different levels and achieves more accurate tracking compared with other robust filters.