Adaptive particle filter for state estimation with application to non‐linear system
Abstract Particle filtering (PF) has certain application value, but the disadvantage is that there is a phenomenon of particle degradation. In order to reduce the impact of this problem, this paper presents a new adaptive PF approach to improve the estimate accuracy. From the perspective of selectin...
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| Format: | Article |
| Language: | English |
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Wiley
2022-12-01
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| Series: | IET Signal Processing |
| Online Access: | https://doi.org/10.1049/sil2.12147 |
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| _version_ | 1832559570473451520 |
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| author | Fangfang Zhao Ruijie Cai |
| author_facet | Fangfang Zhao Ruijie Cai |
| author_sort | Fangfang Zhao |
| collection | DOAJ |
| description | Abstract Particle filtering (PF) has certain application value, but the disadvantage is that there is a phenomenon of particle degradation. In order to reduce the impact of this problem, this paper presents a new adaptive PF approach to improve the estimate accuracy. From the perspective of selecting an appropriate important density functions, in this filter, the particles are first updated using the Spherical Simplex Unscented Kalman Filter algorithm, and then the particles are updated using the Adaptive Extended Kalman filter algorithm. Simultaneously, from the perspective of improving the resampling method, a new resampling technique based on the random resampling method has been designed and fused to this filter. The comparison and analysis of two simulation schemes have been conducted to assess the performance of the designed filtering algorithm. The simulation results show the effectiveness of the proposed approach. |
| format | Article |
| id | doaj-art-9dd08dca8b9d4b8198cea85e37517999 |
| institution | Kabale University |
| issn | 1751-9675 1751-9683 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-9dd08dca8b9d4b8198cea85e375179992025-02-03T01:29:44ZengWileyIET Signal Processing1751-96751751-96832022-12-011691023103310.1049/sil2.12147Adaptive particle filter for state estimation with application to non‐linear systemFangfang Zhao0Ruijie Cai1State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 ChinaState Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 ChinaAbstract Particle filtering (PF) has certain application value, but the disadvantage is that there is a phenomenon of particle degradation. In order to reduce the impact of this problem, this paper presents a new adaptive PF approach to improve the estimate accuracy. From the perspective of selecting an appropriate important density functions, in this filter, the particles are first updated using the Spherical Simplex Unscented Kalman Filter algorithm, and then the particles are updated using the Adaptive Extended Kalman filter algorithm. Simultaneously, from the perspective of improving the resampling method, a new resampling technique based on the random resampling method has been designed and fused to this filter. The comparison and analysis of two simulation schemes have been conducted to assess the performance of the designed filtering algorithm. The simulation results show the effectiveness of the proposed approach.https://doi.org/10.1049/sil2.12147 |
| spellingShingle | Fangfang Zhao Ruijie Cai Adaptive particle filter for state estimation with application to non‐linear system IET Signal Processing |
| title | Adaptive particle filter for state estimation with application to non‐linear system |
| title_full | Adaptive particle filter for state estimation with application to non‐linear system |
| title_fullStr | Adaptive particle filter for state estimation with application to non‐linear system |
| title_full_unstemmed | Adaptive particle filter for state estimation with application to non‐linear system |
| title_short | Adaptive particle filter for state estimation with application to non‐linear system |
| title_sort | adaptive particle filter for state estimation with application to non linear system |
| url | https://doi.org/10.1049/sil2.12147 |
| work_keys_str_mv | AT fangfangzhao adaptiveparticlefilterforstateestimationwithapplicationtononlinearsystem AT ruijiecai adaptiveparticlefilterforstateestimationwithapplicationtononlinearsystem |