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Article: On the Performance Analysis of a Class of Transform-domain NLMS Algorithms with Gaussian Inputs and Mixture Gaussian Additive Noise Environment
| Title | On the Performance Analysis of a Class of Transform-domain NLMS Algorithms with Gaussian Inputs and Mixture Gaussian Additive Noise Environment |
|---|---|
| Authors | |
| Keywords | Adaptive filters Convergence performance analysis Impulsive noise M-estimation TD normalized least mean M-estimate (TDNLMM) Transform domain normalized least mean square (TDNLMS) |
| Issue Date | 2011 |
| Publisher | Springer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/content/120889/ |
| Citation | Journal Of Signal Processing Systems, 2011, v. 64 n. 3, p. 429-445 How to Cite? |
| Abstract | This paper studies the convergence performance of the transform domain normalized least mean square (TDNLMS) algorithm with general nonlinearity and the transform domain normalized least mean M-estimate (TDNLMM) algorithm in Gaussian inputs and additive Gaussian and impulsive noise environment. The TDNLMM algorithm, which is derived from robust M-estimation, has the advantage of improved performance over the conventional TDNLMS algorithm in combating impulsive noises. Using Price's theorem and its extension, the above algorithms can be treated in a single framework respectively for Gaussian and impulsive noise environments. Further, by introducing new special integral functions, related expectations can be evaluated so as to obtain decoupled difference equations which describe the mean and mean square behaviors of the TDNLMS and TDNLMM algorithms. These analytical results reveal the advantages of the TDNLMM algorithm in impulsive noise environment, and are in good agreement with computer simulation results. © 2010 The Author(s). |
| Persistent Identifier | http://hdl.handle.net/10722/145084 |
| ISSN | 2023 Impact Factor: 1.6 2023 SCImago Journal Rankings: 0.479 |
| ISI Accession Number ID | |
| References | Widrow, B., McCool, J., Larimore, M. G., & Johnson, C. R., Jr. (1976). Stationary and nonstationary learning characteristics of the LMS adaptive filter. Proceedings of IEEE, 64, 1151–1162. doi: 10.1109/PROC.1976.10286 Nagumo, J. I., & Noda, A. (1967). A learning method for system identification. IEEE Transactions on Automatic Control, AC-12, 282–287. doi: 10.1109/TAC.1967.1098599 Narayan, S., Peterson, A. M., & Narasimha, M. J. (1983). Transform domain LMS algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-31, 609–615. doi: 10.1109/TASSP.1983.1164121 Lee, J. C., & Un, C. K. (1986). Performance of transform-domain LMS adaptive digital filters. IEEE Transactions on Acoustics, Speech, and Signal Processing, 34(3), 499–510. doi: 10.1109/TASSP.1986.1164850 Boroujeny, B. F., & Gazor, S. (1992). Selection of orthonormal transforms for improving the performance of the transform domain normalized LMS algorithm. IEE Proceedings. Radar and Signal Processing, 139(5), 327–335. doi: 10.1049/ip-f-2.1992.0046 Pei, S. C., & Tseng, C. C. (1996). Transform domain adaptive linear phase filter. IEEE Transactions on Signal Processing, 44(12), 3142–3146. doi: 10.1109/78.553489 Marshall, D. F., Jenkins, W. K., & Murphy, J. J. (1989). The use of orthogonal transforms for improving performance of adaptive filters. IEEE Transactions on Circuits and Systems, 36(4), 474–484. doi: 10.1109/31.92880 Zou, Y., Chan, S. C., & Ng, T. S. (2000). Least mean M-estimate algorithms for robust adaptive filtering in impulsive noise. IEEE Transactions on Circuits and Systems II, 47, 1564–1569. doi: 10.1109/82.899657 Huber, P. J. (1981). Robust statistics. New York: John Wiley. doi: 10.1002/0471725250 Chan, S. C., & Zou, Y. (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 Price, R. (1958). A useful theorem for nonlinear devices having Gaussian inputs. IEEE Transactions on Information Theory, 4(2), 69–72. doi: 10.1109/TIT.1958.1057444 Price, R. (1964). Comment on: ’A useful theorem for nonlinear devices having Gaussian inputs’. IEEE Transactions on Information Theory, IT-10, 171. doi: 10.1109/TIT.1964.1053659 Haweel, T. I., & Clarkson, P. M. (1992). A class of order statistic LMS algorithms. IEEE Transactions on Signal Processing, 40(1), 44–53. doi: 10.1109/78.157180 Koike, S. (1997). Adaptive threshold nonlinear algorithm for adaptive filters with robustness against impulsive noise. IEEE Transactions on Signal Processing, 45(9), 2391–2395. doi: 10.1109/78.622963 Bershad, N. J. (1988). On error-saturation nonlinearities in LMS adaptation. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-36(4), 440–452. doi: 10.1109/29.1548 Mathews, V. J. (1991). Performance analysis of adaptive filters equipped with the dual sign algorithm. IEEE Transactions on Signal Processing, 39, 85–91. doi: 10.1109/78.80768 Koike, S. (2006). Performance analysis of the normalized LMS algorithm for complex-domain adaptive filters in the presence of impulse noise at filter input. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Science, E89-A(9), 2422–2428. doi: 10.1093/ietfec/e89-a.9.2422 Koike, S. (2006). Convergence analysis of adaptive filters using normalized sign-sign algorithm. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Science, E88-A(11), 3218–3224. doi: 10.1093/ietfec/e88-a.11.3218 Bershad, N. J. (1986). Analysis of the normalized LMS algorithm with Gaussian inputs. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-34, 793–806. doi: 10.1109/TASSP.1986.1164914 |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Chan, SC | en_HK |
| dc.contributor.author | Zhou, Y | en_HK |
| dc.date.accessioned | 2012-02-21T05:43:41Z | - |
| dc.date.available | 2012-02-21T05:43:41Z | - |
| dc.date.issued | 2011 | en_HK |
| dc.identifier.citation | Journal Of Signal Processing Systems, 2011, v. 64 n. 3, p. 429-445 | en_HK |
| dc.identifier.issn | 1939-8018 | en_HK |
| dc.identifier.uri | http://hdl.handle.net/10722/145084 | - |
| dc.description.abstract | This paper studies the convergence performance of the transform domain normalized least mean square (TDNLMS) algorithm with general nonlinearity and the transform domain normalized least mean M-estimate (TDNLMM) algorithm in Gaussian inputs and additive Gaussian and impulsive noise environment. The TDNLMM algorithm, which is derived from robust M-estimation, has the advantage of improved performance over the conventional TDNLMS algorithm in combating impulsive noises. Using Price's theorem and its extension, the above algorithms can be treated in a single framework respectively for Gaussian and impulsive noise environments. Further, by introducing new special integral functions, related expectations can be evaluated so as to obtain decoupled difference equations which describe the mean and mean square behaviors of the TDNLMS and TDNLMM algorithms. These analytical results reveal the advantages of the TDNLMM algorithm in impulsive noise environment, and are in good agreement with computer simulation results. © 2010 The Author(s). | en_HK |
| dc.language | eng | en_US |
| dc.publisher | Springer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/content/120889/ | en_HK |
| dc.relation.ispartof | Journal of Signal Processing Systems | en_HK |
| dc.rights | The Author(s) | en_US |
| dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | en_US |
| dc.subject | Adaptive filters | en_HK |
| dc.subject | Convergence performance analysis | en_HK |
| dc.subject | Impulsive noise | en_HK |
| dc.subject | M-estimation | en_HK |
| dc.subject | TD normalized least mean M-estimate (TDNLMM) | en_HK |
| dc.subject | Transform domain normalized least mean square (TDNLMS) | en_HK |
| dc.title | On the Performance Analysis of a Class of Transform-domain NLMS Algorithms with Gaussian Inputs and Mixture Gaussian Additive Noise Environment | en_HK |
| dc.type | Article | en_HK |
| dc.identifier.openurl | http://library.hku.hk:4551/resserv?sid=springerlink&genre=article&atitle=On the Performance Analysis of a Class of Transform-domain NLMS Algorithms with Gaussian Inputs and Mixture Gaussian Additive Noise Environment&title=Journal of Signal Processing Systems&issn=19398018&date=2011-09-01&volume=64&issue=3& spage=429&authors=S. C. Chan, Y. Zhou | en_US |
| dc.identifier.email | Chan, SC: ascchan@hkucc.hku.hk | en_HK |
| dc.identifier.email | Zhou, Y: yizhou@eee.hku.hk | en_HK |
| dc.identifier.authority | Chan, SC=rp00094 | en_HK |
| dc.identifier.authority | Zhou, Y=rp00213 | en_HK |
| dc.description.nature | published_or_final_version | en_US |
| dc.identifier.doi | 10.1007/s11265-010-0494-5 | en_HK |
| dc.identifier.scopus | eid_2-s2.0-84872437535 | en_HK |
| dc.identifier.hkuros | 195838 | - |
| dc.relation.references | Widrow, B., McCool, J., Larimore, M. G., & Johnson, C. R., Jr. (1976). Stationary and nonstationary learning characteristics of the LMS adaptive filter. Proceedings of IEEE, 64, 1151–1162. | en_US |
| dc.relation.references | doi: 10.1109/PROC.1976.10286 | en_US |
| dc.relation.references | Plackett, R. L. (1972). The discovery of the method of least-squares. Biometrika, 59(2), 239–251. | en_US |
| dc.relation.references | Nagumo, J. I., & Noda, A. (1967). A learning method for system identification. IEEE Transactions on Automatic Control, AC-12, 282–287. | en_US |
| dc.relation.references | doi: 10.1109/TAC.1967.1098599 | en_US |
| dc.relation.references | Narayan, S., Peterson, A. M., & Narasimha, M. J. (1983). Transform domain LMS algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-31, 609–615. | en_US |
| dc.relation.references | doi: 10.1109/TASSP.1983.1164121 | en_US |
| dc.relation.references | Lee, J. C., & Un, C. K. (1986). Performance of transform-domain LMS adaptive digital filters. IEEE Transactions on Acoustics, Speech, and Signal Processing, 34(3), 499–510. | en_US |
| dc.relation.references | doi: 10.1109/TASSP.1986.1164850 | en_US |
| dc.relation.references | Boroujeny, B. F., & Gazor, S. (1992). Selection of orthonormal transforms for improving the performance of the transform domain normalized LMS algorithm. IEE Proceedings. Radar and Signal Processing, 139(5), 327–335. | en_US |
| dc.relation.references | doi: 10.1049/ip-f-2.1992.0046 | en_US |
| dc.relation.references | Pei, S. C., & Tseng, C. C. (1996). Transform domain adaptive linear phase filter. IEEE Transactions on Signal Processing, 44(12), 3142–3146. | en_US |
| dc.relation.references | doi: 10.1109/78.553489 | en_US |
| dc.relation.references | Marshall, D. F., Jenkins, W. K., & Murphy, J. J. (1989). The use of orthogonal transforms for improving performance of adaptive filters. IEEE Transactions on Circuits and Systems, 36(4), 474–484. | en_US |
| dc.relation.references | doi: 10.1109/31.92880 | en_US |
| dc.relation.references | Zou, Y., Chan, S. C., & Ng, T. S. (2000). Least mean M-estimate algorithms for robust adaptive filtering in impulsive noise. IEEE Transactions on Circuits and Systems II, 47, 1564–1569. | en_US |
| dc.relation.references | doi: 10.1109/82.899657 | en_US |
| dc.relation.references | Huber, P. J. (1981). Robust statistics. New York: John Wiley. | en_US |
| dc.relation.references | doi: 10.1002/0471725250 | en_US |
| dc.relation.references | Chan, S. C., & Zou, Y. (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. | en_US |
| dc.relation.references | doi: 10.1109/TSP.2004.823496 | en_US |
| dc.relation.references | Price, R. (1958). A useful theorem for nonlinear devices having Gaussian inputs. IEEE Transactions on Information Theory, 4(2), 69–72. | en_US |
| dc.relation.references | doi: 10.1109/TIT.1958.1057444 | en_US |
| dc.relation.references | Price, R. (1964). Comment on: ’A useful theorem for nonlinear devices having Gaussian inputs’. IEEE Transactions on Information Theory, IT-10, 171. | en_US |
| dc.relation.references | doi: 10.1109/TIT.1964.1053659 | en_US |
| dc.relation.references | Haweel, T. I., & Clarkson, P. M. (1992). A class of order statistic LMS algorithms. IEEE Transactions on Signal Processing, 40(1), 44–53. | en_US |
| dc.relation.references | doi: 10.1109/78.157180 | en_US |
| dc.relation.references | Koike, S. (1997). Adaptive threshold nonlinear algorithm for adaptive filters with robustness against impulsive noise. IEEE Transactions on Signal Processing, 45(9), 2391–2395. | en_US |
| dc.relation.references | doi: 10.1109/78.622963 | en_US |
| dc.relation.references | Bershad, N. J. (1988). On error-saturation nonlinearities in LMS adaptation. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-36(4), 440–452. | en_US |
| dc.relation.references | doi: 10.1109/29.1548 | en_US |
| dc.relation.references | Mathews, V. J. (1991). Performance analysis of adaptive filters equipped with the dual sign algorithm. IEEE Transactions on Signal Processing, 39, 85–91. | en_US |
| dc.relation.references | doi: 10.1109/78.80768 | en_US |
| dc.relation.references | Koike, S. (2006). Performance analysis of the normalized LMS algorithm for complex-domain adaptive filters in the presence of impulse noise at filter input. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Science, E89-A(9), 2422–2428. | en_US |
| dc.relation.references | doi: 10.1093/ietfec/e89-a.9.2422 | en_US |
| dc.relation.references | Koike, S. (2006). Convergence analysis of adaptive filters using normalized sign-sign algorithm. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Science, E88-A(11), 3218–3224. | en_US |
| dc.relation.references | doi: 10.1093/ietfec/e88-a.11.3218 | en_US |
| dc.relation.references | Bershad, N. J. (1986). Analysis of the normalized LMS algorithm with Gaussian inputs. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-34, 793–806. | en_US |
| dc.relation.references | doi: 10.1109/TASSP.1986.1164914 | en_US |
| dc.relation.references | Sayed, A. H. (2003). Fundamentals of adaptive filtering. NY: Wiley. | en_US |
| dc.relation.references | Haykin, S. (2001). Adaptive filter theory, 4th edn. Prentice Hall Press. | en_US |
| dc.relation.references | Boroujeny, B. F. (1998). Adaptive filters: theory and applications. John Wiley & Sons. | en_US |
| dc.relation.references | Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., & Stahel, W. A. (2005). Robust statistics: the approach based on influence functions. New York: Wiley. | en_US |
| dc.relation.references | Chan, S. C., & Zhou, Y. (2007). On the convergence analysis of the normalized LMS and the normalized least mean M-estimate algorithms. In: Proc. IEEE Int. Symp. Signal Processing and Information Technology (pp. 1059–1065). | en_US |
| dc.relation.references | Settineri, R., Najim, M., & Ottaviani, D. (1996). Order statistic fast Kalman filter. Proceedings of IEEE International Symposium on Circuits and Systems, 2, 116–119. | en_US |
| dc.relation.references | Weng, J. F., & Leung, S. H. (1997). Adaptive nonlinear RLS algorithm for robust filtering in impulsive noise. Proceedings of IEEE International Symposium on Circuits and Systems, 4, 2337–2340. | en_US |
| dc.relation.references | Tukey, J. W. (1960). A survey of sampling from contaminated distributions in contributions to probability and statistics: I. In: Olkin (Ed.) Stanford University Press. | en_US |
| dc.relation.references | Recktenwald, G. (2000). Numerical methods with MATLAB: implementations and applications. Englewood Cliffs: Prentice-Hall. | en_US |
| dc.relation.references | Zhou, Y. (2006). Improved analysis and design of efficient adaptive transversal filtering algorithms with particular emphasis on noise, input and channel modeling. Ph. D. Dissertation, The Univ. Hong Kong, Hong Kong. | en_US |
| dc.relation.references | Chan, S. C., & Zhou, Y. (Dec. 2008). On the convergence analysis of the transform domain normalized LMS and related M-estimate algorithms. Proc. IEEE APCCAS 2008, 205–208. | en_US |
| dc.identifier.volume | 64 | en_US |
| dc.identifier.issue | 3 | en_US |
| dc.identifier.spage | 429 | en_HK |
| dc.identifier.epage | 445 | en_HK |
| dc.identifier.eissn | 1939-8115 | en_US |
| dc.identifier.isi | WOS:000293711100012 | - |
| dc.publisher.place | United States | en_HK |
| dc.description.other | Springer Open Choice, 21 Feb 2012 | en_US |
| dc.identifier.scopusauthorid | Chan, SC=13310287100 | en_HK |
| dc.identifier.scopusauthorid | Zhou, Y=55209555200 | en_HK |
| dc.identifier.citeulike | 7376732 | - |
| dc.identifier.issnl | 1939-8115 | - |