Complex-valued Normalized Maximum Versoria Criterion Algorithm for Widely Linear Adaptive Filter
Pages 232 - 236
Abstract
The wide linear (WL) model promotes the application of the complex domain algorithms more extensive, controlling both the circular and the non-circular signals well. The WL complex least mean square (WL-CLMS) algorithm has been utilized in an assortment of filtering scenarios, and has achieved satisfactory results. However, the output of filter is usually interfered by non-Gaussian noise in the real world, leading to serious performance degradation of the WL-CLMS algorithm. By employing the maximum complex correntropy criterion (MCCC), previous work has presented the widely linear complex-valued estimated-input MCCC (WLC-EIMCCC) algorithm to solve impulsive noise. Nonetheless, the calculation cost of this algorithm is expensive due to the exponential operators, and its steady-state error still has room for improvements. In this work, the maximum complex Versoria criterion (MCVC) is defined according to the concept of complex correntropy. The WL-CMVC algorithm is put forward, of which the steady-state error is lower than the WL-MCCC algorithm. Besides, the normalized form is derived based on the WL-CMVC. Comparative experiments are carried out in system identification scenario wherein the unknown system is interfered with different background measurement noise. Simulation results verify that the proposed algorithms can achieve lower steady-state misadjustment than the WL-MCCC algorithm.
References
[1]
Sen Kuo and Dennis. Morgan, 1999. Active Noise Control Systems: Algorithms and DSP Implementations., Proceedings of the IEEE.
[2]
Eberhard Hänsler, 2003. Acoustic Echo Cancellation, in: Wiley Encyclopedia of Telecommunications. John Wiley & Sons, Inc.
[3]
Lal C. Godara, 2009. “Application of Antenna Arrays to Mobile Communications, Part II: Beam-Forming and Direction-of-Arrival Considerations.” In Adaptive Antennas for Wireless Communications, 95–145. Wiley-IEEE Press. https://doi.org/10.1109/9780470544075.ch2.
[4]
Cédric Richard, José C.M. Bermudez and Paul Honeine, 2009. Online prediction of time series data with kernels. IEEE Transactions on Signal Processing 57, 1058–1067. https://doi.org/10.1109/TSP.2008.2009895
[5]
Bernard Widrow, John McCool, and Michael Ball 1975. The Complex LMS Algorithm. Proceedings of the IEEE 63, 719–720. https://doi.org/10.1109/PROC.1975.9807
[6]
Sundar G. Sankaran and A.A. Beex, 2000. Convergence behavior of affine projection algorithms. IEEE Transactions on Signal Processing 48, 10861096. https://doi.org/10.1109/78.827542
[7]
Wentao, Ma, Dongqiao Zheng, Yuanhao Li, Zhiyu Zhang, and Badong Chen., 2018. Bias-compensated normalized maximum correntropy criterion algorithm for system identification with noisy input. Signal Processing 152, 160–164. https://doi.org/10.1016/j.sigpro.2018.05.029
[8]
Pucha Song, and Haiquan Zhao. 2018. Filtered-x Generalized Mixed Norm (FXGMN) Algorithm for Active Noise Control. Mechanical Systems and Signal Processing 107 (July). Academic Press: 93–104. https://doi.org/10.1016/j.ymssp.2018.01.035.
[9]
Soroush Javidi, Maciej Pedzisz, Su Lee Goh, and Danilo P Mandic. 2008. The Augmented Complex Least Mean Square Algorithm with Application to Adaptive Prediction Problems. In Proceedings of the 1st IARP Workshop on Cognitive Information Processing, 54–57.
[10]
Bernard Picinbono, 1994. “On Circularity.” IEEE Transactions on Signal Processing 42 (12): 3473–82. https://doi.org/10.1109/78.340781.
[11]
Peter J. Schreier and Louis L. Scharf. 2003. “Second-Order Analysis of Improper Complex Random Vectors and Processes.” IEEE Transactions on Signal Processing 51 (3): 714–25. https://doi.org/10.1109/TSP.2002.808085.
[12]
Fredy D. Neeser and James L. Massey. 1993. “Proper Complex Random Processes with Applications to Information Theory.” IEEE Transactions on Information Theory 39 (4): 1293–1302. https://doi.org/10.1109/18.243446.
[13]
Bernard Picinbono and Pascal Bondon. 1997. “Second-Order Statistics of Complex Signals.” IEEE Transactions on Signal Processing 45 (2). Institute of Electrical and Electronics Engineers Inc.: 411–20. https://doi.org/10.1109/78.554305.
[14]
R. Schober, W. H. Gerstacker, and L. H.J. Lampe. 2003. “A Widely Linear LMS Algorithm for MAI Suppression for DS-CDMA.” In IEEE International Conference on Communications, 4:2520–25. https://doi.org/10.1109/icc.2003.1204401.
[15]
Sandra Lagen, Adrian Agustin, and Josep Vidal, 2016. “Coexisting Linear and Widely Linear Transceivers in the MIMO Interference Channel.” IEEE Transactions on Signal Processing 64 (3). Institute of Electrical and Electronics Engineers Inc.: 652–64. https://doi.org/10.1109/TSP.2015.2489604.
[16]
Rickie R. Davis and Odile Clavier. 2017. “Impulsive Noise: A Brief Review.” Hearing Research. Elsevier B.V. https://doi.org/10.1016/j.heares.2016.10.020.
[17]
Peng Li and Xun Yu. 2013. “Active Noise Cancellation Algorithms for Impulsive Noise.” Mechanical Systems and Signal Processing 36 (2): 630–35. https://doi.org/10.1016/j.ymssp.2012.10.017.
[18]
Pucha Song, Haiquan Zhao, and Xiangping Zeng. 2019. “Robust Diffusion Affine Projection Algorithm with Variable Step-Size over Distributed Networks.” IEEE Access 7. Institute of Electrical and Electronics Engineers Inc.: 150484–91. https://doi.org/10.1109/ACCESS.2019.2947636.
[19]
Fuyi Huang, Jiashu Zhang, and Sheng Zhang. 2018. “A Family of Robust Adaptive Filtering Algorithms Based on Sigmoid Cost.” Signal Processing 149 (August). Elsevier B.V.: 179–92. https://doi.org/10.1016/j.sigpro.2018.03.013.
[20]
Weifeng Liu, Puskal P. Pokharel, and Jose C. Principe. 2007. “Correntropy: Properties and Applications in Non-Gaussian Signal Processing.” IEEE Transactions on Signal Processing 55 (11): 5286–98. https://doi.org/10.1109/TSP.2007.896065.
[21]
P.F. Joao, Guimaraes, Aluisio I.R. Fontes, Joilson B.A. Rego, M. Allan De Martins, and Jose C. Principe. 2017. “Complex Correntropy: Probabilistic Interpretation and Application to Complex-Valued Data.” IEEE Signal Processing Letters 24 (1). Institute of Electrical and Electronics Engineers Inc.: 42–45. https://doi.org/10.1109/LSP.2016.2634534.
[22]
Fei Dong, Guobing Qian, and Shiyuan Wang. 2020. “Bias-Compensated MCCC Algorithm for Widely Linear Adaptive Filtering with Noisy Data.” IEEE Transactions on Circuits and Systems II: Express Briefs 67 (12). Institute of Electrical and Electronics Engineers Inc.: 3587–91. https://doi.org/10.1109/TCSII.2020.2995751.
[23]
Yu Xia, Jianchang Liu, and Hongru Li. 2009. “An Adaptive Inertia Weight Particle Swarm Optimization Algorithm for IIR Digital Filter.” In 2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009, 1:114–18. https://doi.org/10.1109/AICI.2009.28.
[24]
Fuyi, Huang, Jiashu Zhang, and Sheng Zhang. 2017. “Maximum Versoria Criterion-Based Robust Adaptive Filtering Algorithm.” IEEE Transactions on Circuits and Systems II: Express Briefs 64 (10). Institute of Electrical and Electronics Engineers Inc.: 1252–56. https://doi.org/10.1109/TCSII.2017.2671521.
Index Terms
- Complex-valued Normalized Maximum Versoria Criterion Algorithm for Widely Linear Adaptive Filter
Index terms have been assigned to the content through auto-classification.
Recommendations
Bias-Compensated MCSE Algorithm for Widely Linear Complex-Valued Adaptive Filtering with Noisy Inputs
ICDSP '22: Proceedings of the 6th International Conference on Digital Signal ProcessingIn this paper, based on minimum complex Shannon entropy (MCSE), a novel widely linear complex-valued estimated-input MCSE (WLC-EIMCSE) algorithm is proposed, which can not only make unbiased estimation in the environment where the input signal has noise,...
Complex Total Least Mean M-Estimate Adaptive Algorithm for Noisy Input and Impulsive Noise
AbstractThe complex domain adaptive filtering algorithms has shown excellent performance in the field of signal processing. Among them, the well-known complex least mean square (CLMS) algorithm has been widely used in practical projects. However, the CLMS ...
Steady-state mean-square deviation analysis of improved normalized subband adaptive filter
A new minimization criterion for the normalized subband adaptive filter (NSAF), which is called improved NSAF (INSAF), was introduced recently to improve the performance of the steady-state mean-square deviation (MSD). However, the steady-state MSD ...
Comments
Information & Contributors
Information
Published In
February 2021
336 pages
ISBN:9781450389365
DOI:10.1145/3458380
Copyright © 2021 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 23 September 2021
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- the National Rail Transportation Electrification and Automation Engineering Technology Research Center
- National Natural Science Foundation of China
- Department of Science and Technology of Sichuan Province
Conference
ICDSP 2021
ICDSP 2021: 2021 5th International Conference on Digital Signal Processing
February 26 - 28, 2021
Chengdu, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 62Total Downloads
- Downloads (Last 12 months)11
- Downloads (Last 6 weeks)2
Reflects downloads up to 28 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format