Abstract
RSOR algoritması, uyarlamalı filtre parametrelerini güncellemek için RLS algoritmasına alternatif olarak önerilmiş olan tekrarlamalı bir algoritmadır. Diğer algoritmalarda olduğu gibi, unutma faktörü, filtre uzunluğu ve gevşetme parametresi RSOR algoritmasının performansını önemli ölçüde etkilemektedir. Bu çalışmada, bir uyarlamalı FIR filtre sistem tanıma modunda kullanılarak unutma faktörünün, filtre uzunluğunun ve gevşetme parametresinin RSOR algoritmasındaki sızıntı olayına etkisi incelenmiştir. Bu amaçla, öncelikle ölçme gürültüsünün uyarlamalı filtre çıkışına etkisi, yani sızıntı olayı, analitik olarak açıklanmış, sonra unutma faktörünün ve diğer filtre parametrelerinin bu sızıntı olayına etkisi incelenmiştir. Yapılan benzetim çalışmalarıyla elde edilen sonuçlar, benzer algoritmalar ile karşılaştırılmıştır.
References
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- Chan, S.-C., Zou, Z.-X. (2004) A recursive least M-estimate algorithm for robust adaptive filtering in impulsive noise: fast algorithm and convergence performance analysis. IEEE Transactions on Signal Processing, 52(4), 975-991. doi: 10.1109/TSP.2004.823496
- Ciochină, S., Paleologu, C., Benesty, J., Enescu, A.A. (2009) On the influence of the forgetting factor of the RLS adaptive filter in system identification, International Symposium on Signals, Circuits and Systems (ISSCS 2009), Iasi, Romania, 1-4. doi: 10.1109/ISSCS.2009.5206117
- Diniz, P.S.R. (2013) Adaptive Filtering: Algorithms and Practical Implementation (4th ed.), Springer, New York.
- Golub, G.H., Van Loan, C.F. (1996) Matrix Computations (3rd ed.), John Hopkins University Press, Baltimore.
- Hatun, M., Koçal, O.H. (2012) Recursive successive over-relaxation algorithm for adaptive filtering, The 5th International Conference on Communications, Computers and Applications (MIC-CCA2012), İstanbul, Turkey, 90-95.
- Hatun, M., Koçal, O.H. (2017) Stochastic convergence analysis of recursive successive over-relaxation algorithm in adaptive filtering, Signal, Image and Video Processing, 11(1), 137-144. doi: 10.1007/s11760-016-0912-7
- Haykin, S. (2002) Adaptive Filter Theory (4th ed.), Prentice-Hall, New Jersey.
- Paleologu, C., Benesty, J. Ciochină, S. (2008) A robust variable forgetting factor recursive least-squares algorithm for system identification, IEEE Signal Processing Letters, 15, 597-600. doi: 10.1109/LSP.2008.2001559
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Abstract
The RSOR algorithm is a recursive algorithm that has been proposed as an alternative to the RLS algorithm for updating adaptive filter parameters. As with other algorithms, the forgetting factor, filter length and relaxation parameter significantly affects the performance of the RSOR algorithm. In this study, using an adaptive FIR filter in system identification mode, the effect of forgetting factor, filter length and relaxation parameter on the leakage phenomenon of the RSOR algorithm was analyzed. For this purpose, firstly, the effect of measurement noise on the adaptive filter output, namely the leakage phenomenon, was explained analytically, and then the influence of the forgetting factor and other filter parameters on this leakage phenomenon was examined. The results obtained from the simulation studies are compared with similar algorithms.
References
- Ahmad, M.S., Kukrer, O., Hocanin, A. (2011a) Recursive inverse adaptive filtering algorithm, Digital Signal Processing, 21(4), 491-496. doi: 10.1016/j.dsp.2011.03.001
- Ahmad, M.S., Kukrer, O., Hocanin, A. (2011b) The effect of the forgetting factor on the RI adaptive algorithm in system identification, International Symposium on Signals, Circuits and Systems (ISSCS 2011), Iasi, Romania, 1-4. doi: 10.1109/ISSCS.2011.5978751
- Chan, S.-C., Zou, Z.-X. (2004) A recursive least M-estimate algorithm for robust adaptive filtering in impulsive noise: fast algorithm and convergence performance analysis. IEEE Transactions on Signal Processing, 52(4), 975-991. doi: 10.1109/TSP.2004.823496
- Ciochină, S., Paleologu, C., Benesty, J., Enescu, A.A. (2009) On the influence of the forgetting factor of the RLS adaptive filter in system identification, International Symposium on Signals, Circuits and Systems (ISSCS 2009), Iasi, Romania, 1-4. doi: 10.1109/ISSCS.2009.5206117
- Diniz, P.S.R. (2013) Adaptive Filtering: Algorithms and Practical Implementation (4th ed.), Springer, New York.
- Golub, G.H., Van Loan, C.F. (1996) Matrix Computations (3rd ed.), John Hopkins University Press, Baltimore.
- Hatun, M., Koçal, O.H. (2012) Recursive successive over-relaxation algorithm for adaptive filtering, The 5th International Conference on Communications, Computers and Applications (MIC-CCA2012), İstanbul, Turkey, 90-95.
- Hatun, M., Koçal, O.H. (2017) Stochastic convergence analysis of recursive successive over-relaxation algorithm in adaptive filtering, Signal, Image and Video Processing, 11(1), 137-144. doi: 10.1007/s11760-016-0912-7
- Haykin, S. (2002) Adaptive Filter Theory (4th ed.), Prentice-Hall, New Jersey.
- Paleologu, C., Benesty, J. Ciochină, S. (2008) A robust variable forgetting factor recursive least-squares algorithm for system identification, IEEE Signal Processing Letters, 15, 597-600. doi: 10.1109/LSP.2008.2001559
- Salman, M.S., Kukrer, O., Hocanin, A. (2017) Recursive inverse algorithm: mean-square-error analysis, Digital Signal Processing, 66, 10-17. doi: 10.1016/j.dsp.2017.04.001
Details
| Primary Language | English |
|---|---|
| Subjects | Electrical Engineering |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | April 30, 2021 |
| Submission Date | October 3, 2020 |
| Acceptance Date | April 9, 2021 |
| Published in Issue | Year 2021 Volume: 26 Issue: 1 |
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