Abstract
This paper proposes a recursive constrained maximum Versoria criterion (RCMVC) algorithm. In comparison with recursive competing methods, our proposed RCMVC can achieve smaller steady-state misalignment in non-Gaussian noisy environments. Specifically, we use the maximum Versoria criterion (MVC) to derive a new robust recursive constrained adaptive filtering within the least-squares framework for solving linearly constrained problems. For RCMVC, we analyze the mean-square stability and characterize the theoretical transient mean square deviation (MSD) performance. Furthermore, we conduct some simulations to validate the consistency between the analytical and simulation results and show the effectiveness of RCMVC in non-Gaussian noisy environments.
This work is supported in part by the National Natural Science Foundation of China (Grant no. 62201478 and 61971100), in part by the Southwest University of Science and Technology Doctor Fund (Grant no. 20zx7119), in part by the Sichuan Science and Technology Program (Grant no. 2022YFG0148), and in part by the Heilongjiang Provincial Science and Technology Program (No. 2022ZX01A16).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Peng, S., Chen, B., Sun, L., Ser, W., Lin, Z.: Constrained maximum correntropy adaptive filtering. Signal Process. 140, 116–126 (2017)
de Campos, M.L.R., Werner, S., Apolinário, J.A.: Constrained adaptive filters. In: Chandran, S. (eds.) Adaptive Antenna Arrays. Signals and Communication Technology. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-662-05592-2_3
Arablouei, R., Dogancay, K.: Reduced-complexity constrained recursive least-squares adaptive filtering algorithm. IEEE Trans. Signal Process. 60(12), 6687–6692 (2012)
Arablouei, R., Dogancay, K., Werner, S.: On the mean-square performance of the constrained LMS algorithm. Signal Process. 117(Dec.), 192–197 (2015)
Lee, K., Baek, Y., Park, Y.: Nonlinear acoustic echo cancellation using a nonlinear postprocessor with a linearly constrained affine projection algorithm. IEEE Trans. Circuits Syst. II Express Briefs 62(9), 881–885 (2015)
Kumar, K., Pandey, R., Karthik, M.L.N.S., Bhattacharjee, S.S., George, N.V.: Robust and sparsity-aware adaptive filters: a review. Signal Process. 189, 108276 (2021)
Peng, S., Ser, W., Chen, B., Sun, L., Lin, Z.: Robust constrained adaptive filtering under minimum error entropy criterion. IEEE Trans. Circuits Syst. II Express Briefs 65(8), 1119–1123 (2018)
Liang, T., Li, Y., Xia, Y.: Recursive constrained adaptive algorithm under q-Renyi Kernel function. IEEE Trans. Circuits Syst. II Express Briefs 68(6), 2227–2231 (2021)
Liang, T., Li, Y., Xue, W., Li, Y., Jiang, T.: Performance and analysis of recursive constrained least lncosh algorithm under impulsive noises. IEEE Trans. Circuits Syst. II Express Briefs 68(6), 2217–2221 (2021)
Zhao, J., Zhang, J.A., Li, Q., Zhang, H., Wang, X.: Recursive constrained generalized maximum correntropy algorithms for adaptive filtering. Signal Process. 199, 108611 (2022)
Wenjing, X., Zhao, H.: Robust constrained recursive least M-estimate adaptive filtering algorithm. Signal Process. 194, 108433 (2022)
Huang, F., Zhang, J., Zhang, S.: Maximum Versoria criterion-based robust adaptive filtering algorithm. IEEE Trans. Circuits Syst. II Express Briefs 64(10), 1252–1256 (2017)
Chen, B., Xing, L., Zhao, H., Zheng, N., PrÃncipe, J.C.: Generalized correntropy for robust adaptive filtering. IEEE Trans. Signal Process. 64(13), 3376–3387 (2016)
Bhattacharjee, S.S., Shaikh, M.A., Kumar, K., George, N.V.: Robust constrained generalized correntropy and maximum Versoria criterion adaptive filters. IEEE Trans. Circuits Syst. II Express Briefs 68(8), 3002–3006 (2021)
Akhtar, M.T., Albu, F., Nishihara, A.: Maximum Versoria-criterion (MVC)-based adaptive filtering method for mitigating acoustic feedback in hearing-aid devices. Appl. Acoust. 181, 108156 (2021)
Ren, C., Wang, Z., Zhao, Z.: A new variable step-size affine projection sign algorithm based on a posteriori estimation error analysis. Circuits Syst. Signal Process. 36(5), 1989–2011 (2017)
Qian, G., Ning, X., Wang, S.: Recursive constrained maximum correntropy criterion algorithm for adaptive filtering. IEEE Trans. Circuits Syst. II Express Briefs 67(10), 2229–2233 (2020)
Radhika, S., Albu, F., Chandrasekar, A.: Steady state mean square analysis of standard maximum Versoria criterion based adaptive algorithm. IEEE Trans. Circuits Syst. II Express Briefs 68(4), 1547–1551 (2021)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, L., Zhao, J., Li, Q., Tang, L., Zhang, H. (2024). Recursive Constrained Maximum Versoria Criterion Algorithm for Adaptive Filtering. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1961. Springer, Singapore. https://doi.org/10.1007/978-981-99-8126-7_34
Download citation
DOI: https://doi.org/10.1007/978-981-99-8126-7_34
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8125-0
Online ISBN: 978-981-99-8126-7
eBook Packages: Computer ScienceComputer Science (R0)