Absolute finite differences based variable forgetting factor RLS algorithm
Abstract Adaptive signal processing requires an efficient non‐stationarity detector. Most of the known non‐stationarity detection algorithms are based on residual statistics. The study proposes a novel non‐stationarity detection algorithm based on finite differences analysis of the processed signal....
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-IET
2022-02-01
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| Series: | IET Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/sil2.12074 |
| _version_ | 1797431595274076160 |
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| author | Slobodan Drašković Željko Đurović Vera Petrović |
| author_facet | Slobodan Drašković Željko Đurović Vera Petrović |
| author_sort | Slobodan Drašković |
| collection | DOAJ |
| description | Abstract Adaptive signal processing requires an efficient non‐stationarity detector. Most of the known non‐stationarity detection algorithms are based on residual statistics. The study proposes a novel non‐stationarity detection algorithm based on finite differences analysis of the processed signal. It also includes a suitable procedure for the forgetting factor design in the adaptation process. The performance of the proposed algorithm is experimentally compared with other known algorithms with regard to slow changes in signal stationarity as well as the influence of free coefficient selection on the quality of estimation. The developed algorithm exhibits the ability to effectively track both slow and abrupt changes in signal stationarity, with a small steady‐state error. The coefficients that need to be set during the application of this algorithm are given intuitive physical meaning. Simulations show good resistance to low signal‐to‐noise ratio and abrupt changes in noise variance. The results also demonstrate estimation performance relative to the water level signal from a thermal power plant steam separator. |
| first_indexed | 2024-03-09T09:47:25Z |
| format | Article |
| id | doaj.art-f6a1960b9fcf4d9eb880c7743bee2830 |
| institution | Directory Open Access Journal |
| issn | 1751-9675 1751-9683 |
| language | English |
| last_indexed | 2024-03-09T09:47:25Z |
| publishDate | 2022-02-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj.art-f6a1960b9fcf4d9eb880c7743bee28302023-12-02T00:31:36ZengHindawi-IETIET Signal Processing1751-96751751-96832022-02-01161809110.1049/sil2.12074Absolute finite differences based variable forgetting factor RLS algorithmSlobodan Drašković0Željko Đurović1Vera Petrović2School of Electrical Engineering University of Belgrade Belgrade SerbiaSchool of Electrical Engineering University of Belgrade Belgrade SerbiaSchool of Electrical and Computer Engineering Academy of Technical and Art Applied Studies Belgrade SerbiaAbstract Adaptive signal processing requires an efficient non‐stationarity detector. Most of the known non‐stationarity detection algorithms are based on residual statistics. The study proposes a novel non‐stationarity detection algorithm based on finite differences analysis of the processed signal. It also includes a suitable procedure for the forgetting factor design in the adaptation process. The performance of the proposed algorithm is experimentally compared with other known algorithms with regard to slow changes in signal stationarity as well as the influence of free coefficient selection on the quality of estimation. The developed algorithm exhibits the ability to effectively track both slow and abrupt changes in signal stationarity, with a small steady‐state error. The coefficients that need to be set during the application of this algorithm are given intuitive physical meaning. Simulations show good resistance to low signal‐to‐noise ratio and abrupt changes in noise variance. The results also demonstrate estimation performance relative to the water level signal from a thermal power plant steam separator.https://doi.org/10.1049/sil2.12074absolute finite differencesnon‐stationarity detectionrecursive least squaresvariable forgetting factor |
| spellingShingle | Slobodan Drašković Željko Đurović Vera Petrović Absolute finite differences based variable forgetting factor RLS algorithm IET Signal Processing absolute finite differences non‐stationarity detection recursive least squares variable forgetting factor |
| title | Absolute finite differences based variable forgetting factor RLS algorithm |
| title_full | Absolute finite differences based variable forgetting factor RLS algorithm |
| title_fullStr | Absolute finite differences based variable forgetting factor RLS algorithm |
| title_full_unstemmed | Absolute finite differences based variable forgetting factor RLS algorithm |
| title_short | Absolute finite differences based variable forgetting factor RLS algorithm |
| title_sort | absolute finite differences based variable forgetting factor rls algorithm |
| topic | absolute finite differences non‐stationarity detection recursive least squares variable forgetting factor |
| url | https://doi.org/10.1049/sil2.12074 |
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