Adaptive Fading-Memory Receding-Horizon Filters and Smoother for Linear Discrete Time-Varying Systems
In this paper, an adaptive fading-memory receding-horizon (AFMRH) filter is proposed by combining the receding-horizon structure and the adaptive fading-memory method. In the recent finite horizon, state error covariance is adapted with an adaptive fading factor; then the process noise covariance ma...
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| Format: | Article |
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MDPI AG
2022-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/13/6692 |
| _version_ | 1797480831621529600 |
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| author | Bokyu Kwon |
| author_facet | Bokyu Kwon |
| author_sort | Bokyu Kwon |
| collection | DOAJ |
| description | In this paper, an adaptive fading-memory receding-horizon (AFMRH) filter is proposed by combining the receding-horizon structure and the adaptive fading-memory method. In the recent finite horizon, state error covariance is adapted with an adaptive fading factor; then the process noise covariance matrix adaption is realized by adjusting the properties of systems. An AFMRH fixed-lag smoother is also proposed by combining the proposed AFMRH filtering algorithm and a Rauch–Tung–Striebel smoothing algorithm to improve the estimation accuracy. Because the proposed AFMRH filter and smoother are reduced to the optimal receding-horizon (RH) filter and smoother when all measurements have the same weight, the proposed adaptive RH estimators could provide a more general solution than the optimal RH filter and smoother. To reduce the complexity and improve the estimation performance of the proposed RH estimators, an adaptive horizon adjustment method and a switching filtering algorithm based on an adaptive fading factor are also proposed. In particular, the proposed adaptive horizon adjustment method is designed to be computationally efficient, which makes it suitable for online and real-time applications. Through computer simulation, the performance and adaptiveness of the proposed approaches were verified by comparing them with existing fading-memory approaches. |
| first_indexed | 2024-03-09T22:05:50Z |
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| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:05:50Z |
| publishDate | 2022-07-01 |
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| spelling | doaj.art-a63a90a03aff414eb0c3a13a5ee961592023-11-23T19:41:07ZengMDPI AGApplied Sciences2076-34172022-07-011213669210.3390/app12136692Adaptive Fading-Memory Receding-Horizon Filters and Smoother for Linear Discrete Time-Varying SystemsBokyu Kwon0Division of Electrical, Control and Instrumentation Engineering, Kangwon National University, Samcheok-si 25913, Gangwon-do, KoreaIn this paper, an adaptive fading-memory receding-horizon (AFMRH) filter is proposed by combining the receding-horizon structure and the adaptive fading-memory method. In the recent finite horizon, state error covariance is adapted with an adaptive fading factor; then the process noise covariance matrix adaption is realized by adjusting the properties of systems. An AFMRH fixed-lag smoother is also proposed by combining the proposed AFMRH filtering algorithm and a Rauch–Tung–Striebel smoothing algorithm to improve the estimation accuracy. Because the proposed AFMRH filter and smoother are reduced to the optimal receding-horizon (RH) filter and smoother when all measurements have the same weight, the proposed adaptive RH estimators could provide a more general solution than the optimal RH filter and smoother. To reduce the complexity and improve the estimation performance of the proposed RH estimators, an adaptive horizon adjustment method and a switching filtering algorithm based on an adaptive fading factor are also proposed. In particular, the proposed adaptive horizon adjustment method is designed to be computationally efficient, which makes it suitable for online and real-time applications. Through computer simulation, the performance and adaptiveness of the proposed approaches were verified by comparing them with existing fading-memory approaches.https://www.mdpi.com/2076-3417/12/13/6692adaptive receding-horizon estimationadaptive fading-memory estimationadaptive horizon adjustmentRauch–Tung–Striebel smoothing |
| spellingShingle | Bokyu Kwon Adaptive Fading-Memory Receding-Horizon Filters and Smoother for Linear Discrete Time-Varying Systems Applied Sciences adaptive receding-horizon estimation adaptive fading-memory estimation adaptive horizon adjustment Rauch–Tung–Striebel smoothing |
| title | Adaptive Fading-Memory Receding-Horizon Filters and Smoother for Linear Discrete Time-Varying Systems |
| title_full | Adaptive Fading-Memory Receding-Horizon Filters and Smoother for Linear Discrete Time-Varying Systems |
| title_fullStr | Adaptive Fading-Memory Receding-Horizon Filters and Smoother for Linear Discrete Time-Varying Systems |
| title_full_unstemmed | Adaptive Fading-Memory Receding-Horizon Filters and Smoother for Linear Discrete Time-Varying Systems |
| title_short | Adaptive Fading-Memory Receding-Horizon Filters and Smoother for Linear Discrete Time-Varying Systems |
| title_sort | adaptive fading memory receding horizon filters and smoother for linear discrete time varying systems |
| topic | adaptive receding-horizon estimation adaptive fading-memory estimation adaptive horizon adjustment Rauch–Tung–Striebel smoothing |
| url | https://www.mdpi.com/2076-3417/12/13/6692 |
| work_keys_str_mv | AT bokyukwon adaptivefadingmemoryrecedinghorizonfiltersandsmootherforlineardiscretetimevaryingsystems |