Multipath Estimation Algorithm Based on Improved Unscented Kalman Filter
Purposes In navigation and positioning system, the multipath estimation algorithms based on Kalman filter framework can effectively improve the positioning accuracy. When the initial value of the process noise and observation noise covariance of such algorithms is improperly selected, a large error...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Editorial Office of Journal of Taiyuan University of Technology
2023-09-01
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| Series: | Taiyuan Ligong Daxue xuebao |
| Subjects: | |
| Online Access: | https://tyutjournal.tyut.edu.cn/englishpaper/show-2120.html |
| _version_ | 1827283437728301056 |
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| author | Jinheng ZHANG Lan CHENG Jing ZHANG Zihang NI Gaowei YAN |
| author_facet | Jinheng ZHANG Lan CHENG Jing ZHANG Zihang NI Gaowei YAN |
| author_sort | Jinheng ZHANG |
| collection | DOAJ |
| description | Purposes In navigation and positioning system, the multipath estimation algorithms based on Kalman filter framework can effectively improve the positioning accuracy. When the initial value of the process noise and observation noise covariance of such algorithms is improperly selected, a large error or even divergence of the estimation results may occur. In addition, because the algorithm is based on the minimum mean squared error criterion, it is susceptible to non-Gaussian noise, especially under heavy-tailed non-Gaussian noise, which has the problem of significant degradation of estimation accuracy. Methods In order to maintain good multipath estimation results under both Gaussian noise and non-Gaussian noise and improve positioning accuracy, an adaptive maximum correntropy unscented Kalman Filter (AMCUKF) multipath estimation algorithm is proposed in this paper. The AMCUKF algorithm introduces the maximum correntropy as an optimization criterion in the process of observation update to solve the problem of estimation accuracy degradation under non-Gaussian noise. In the process of noise covariance update, the residual sequence of the observed quantity is used to recursively update the noise covariance to solve the improper initial value selection of the process noise and the observed noise covariance. Findings Simulation experiments are carried out under Gaussian noise and non-Gaussian noise, and by comparing with two estimation algorithms based on Kalman filter framework, it is shown that AMCUKF multipath algorithm can not only maintain better multipath estimation results under Gaussian noise, but also maintain higher multipath estimation accuracy under non-Gaussian noise, effectively suppressing the interference of non-Gaussian noise. |
| first_indexed | 2024-04-24T09:36:27Z |
| format | Article |
| id | doaj.art-6e494cb62e0f4c98ae192691ee24d3d8 |
| institution | Directory Open Access Journal |
| issn | 1007-9432 |
| language | English |
| last_indexed | 2024-04-24T09:36:27Z |
| publishDate | 2023-09-01 |
| publisher | Editorial Office of Journal of Taiyuan University of Technology |
| record_format | Article |
| series | Taiyuan Ligong Daxue xuebao |
| spelling | doaj.art-6e494cb62e0f4c98ae192691ee24d3d82024-04-15T09:17:01ZengEditorial Office of Journal of Taiyuan University of TechnologyTaiyuan Ligong Daxue xuebao1007-94322023-09-0154587788410.16355/j.tyut.1007-9432.2023.05.0161007-9432(2023)05-0877-08Multipath Estimation Algorithm Based on Improved Unscented Kalman FilterJinheng ZHANG0Lan CHENG1Jing ZHANG2Zihang NI3Gaowei YAN4College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaPurposes In navigation and positioning system, the multipath estimation algorithms based on Kalman filter framework can effectively improve the positioning accuracy. When the initial value of the process noise and observation noise covariance of such algorithms is improperly selected, a large error or even divergence of the estimation results may occur. In addition, because the algorithm is based on the minimum mean squared error criterion, it is susceptible to non-Gaussian noise, especially under heavy-tailed non-Gaussian noise, which has the problem of significant degradation of estimation accuracy. Methods In order to maintain good multipath estimation results under both Gaussian noise and non-Gaussian noise and improve positioning accuracy, an adaptive maximum correntropy unscented Kalman Filter (AMCUKF) multipath estimation algorithm is proposed in this paper. The AMCUKF algorithm introduces the maximum correntropy as an optimization criterion in the process of observation update to solve the problem of estimation accuracy degradation under non-Gaussian noise. In the process of noise covariance update, the residual sequence of the observed quantity is used to recursively update the noise covariance to solve the improper initial value selection of the process noise and the observed noise covariance. Findings Simulation experiments are carried out under Gaussian noise and non-Gaussian noise, and by comparing with two estimation algorithms based on Kalman filter framework, it is shown that AMCUKF multipath algorithm can not only maintain better multipath estimation results under Gaussian noise, but also maintain higher multipath estimation accuracy under non-Gaussian noise, effectively suppressing the interference of non-Gaussian noise.https://tyutjournal.tyut.edu.cn/englishpaper/show-2120.htmlnon-gaussian noisemultipath estimationunscented kalman filtermaximum correntropy criterion |
| spellingShingle | Jinheng ZHANG Lan CHENG Jing ZHANG Zihang NI Gaowei YAN Multipath Estimation Algorithm Based on Improved Unscented Kalman Filter Taiyuan Ligong Daxue xuebao non-gaussian noise multipath estimation unscented kalman filter maximum correntropy criterion |
| title | Multipath Estimation Algorithm Based on Improved Unscented Kalman Filter |
| title_full | Multipath Estimation Algorithm Based on Improved Unscented Kalman Filter |
| title_fullStr | Multipath Estimation Algorithm Based on Improved Unscented Kalman Filter |
| title_full_unstemmed | Multipath Estimation Algorithm Based on Improved Unscented Kalman Filter |
| title_short | Multipath Estimation Algorithm Based on Improved Unscented Kalman Filter |
| title_sort | multipath estimation algorithm based on improved unscented kalman filter |
| topic | non-gaussian noise multipath estimation unscented kalman filter maximum correntropy criterion |
| url | https://tyutjournal.tyut.edu.cn/englishpaper/show-2120.html |
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