Yingying Zhu
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.
- Sen Kuo and Dennis. Morgan, 1999. Active Noise Control Systems: Algorithms and DSP Implementations., Proceedings of the IEEE.Google Scholar
- Eberhard Hänsler, 2003. Acoustic Echo Cancellation, in: Wiley Encyclopedia of Telecommunications. John Wiley & Sons, Inc.Google Scholar
- 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.Google Scholar
- 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.2008.2009895Google ScholarDigital Library
- 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.1975.9807Google ScholarCross Ref
- Sundar G. Sankaran and A.A. Beex, 2000. Convergence behavior of affine projection algorithms. IEEE Transactions on Signal Processing 48, 10861096. https://doi.org/10.1109/78.827542Google ScholarDigital Library
- 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.2018.05.029Google ScholarDigital Library
- 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.Google Scholar
- 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.Google Scholar
- Bernard Picinbono, 1994. “On Circularity.” IEEE Transactions on Signal Processing 42 (12): 3473–82. https://doi.org/10.1109/78.340781.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- 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.Google Scholar
Index Terms
- Complex-valued Normalized Maximum Versoria Criterion Algorithm for Widely Linear Adaptive Filter
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