Research Article
BibTex RIS Cite

RSOR Algoritmasının Sızıntı Analizi

Year 2021, Volume: 26 Issue: 1, 325 - 344, 30.04.2021
https://doi.org/10.17482/uumfd.804785

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

  • 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

LEAKAGE ANALYSIS OF THE RSOR ALGORITHM

Year 2021, Volume: 26 Issue: 1, 325 - 344, 30.04.2021
https://doi.org/10.17482/uumfd.804785

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
There are 11 citations in total.

Details

Primary Language English
Subjects Electrical Engineering
Journal Section Research Articles
Authors

Metin Hatun 0000-0003-0279-5508

Publication Date April 30, 2021
Submission Date October 3, 2020
Acceptance Date April 9, 2021
Published in Issue Year 2021 Volume: 26 Issue: 1

Cite

APA Hatun, M. (2021). LEAKAGE ANALYSIS OF THE RSOR ALGORITHM. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 26(1), 325-344. https://doi.org/10.17482/uumfd.804785
AMA Hatun M. LEAKAGE ANALYSIS OF THE RSOR ALGORITHM. UUJFE. April 2021;26(1):325-344. doi:10.17482/uumfd.804785
Chicago Hatun, Metin. “LEAKAGE ANALYSIS OF THE RSOR ALGORITHM”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 26, no. 1 (April 2021): 325-44. https://doi.org/10.17482/uumfd.804785.
EndNote Hatun M (April 1, 2021) LEAKAGE ANALYSIS OF THE RSOR ALGORITHM. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 26 1 325–344.
IEEE M. Hatun, “LEAKAGE ANALYSIS OF THE RSOR ALGORITHM”, UUJFE, vol. 26, no. 1, pp. 325–344, 2021, doi: 10.17482/uumfd.804785.
ISNAD Hatun, Metin. “LEAKAGE ANALYSIS OF THE RSOR ALGORITHM”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 26/1 (April 2021), 325-344. https://doi.org/10.17482/uumfd.804785.
JAMA Hatun M. LEAKAGE ANALYSIS OF THE RSOR ALGORITHM. UUJFE. 2021;26:325–344.
MLA Hatun, Metin. “LEAKAGE ANALYSIS OF THE RSOR ALGORITHM”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, vol. 26, no. 1, 2021, pp. 325-44, doi:10.17482/uumfd.804785.
Vancouver Hatun M. LEAKAGE ANALYSIS OF THE RSOR ALGORITHM. UUJFE. 2021;26(1):325-44.

Announcements:

30.03.2021-Beginning with our April 2021 (26/1) issue, in accordance with the new criteria of TR-Dizin, the Declaration of Conflict of Interest and the Declaration of Author Contribution forms fulfilled and signed by all authors are required as well as the Copyright form during the initial submission of the manuscript. Furthermore two new sections, i.e. ‘Conflict of Interest’ and ‘Author Contribution’, should be added to the manuscript. Links of those forms that should be submitted with the initial manuscript can be found in our 'Author Guidelines' and 'Submission Procedure' pages. The manuscript template is also updated. For articles reviewed and accepted for publication in our 2021 and ongoing issues and for articles currently under review process, those forms should also be fulfilled, signed and uploaded to the system by authors.