Abstract
In this paper, we propose a novel affine projection sign subband adaptive filter (NAPSSAF) algorithm which can obtain better performance than the conventional APSSAF. The proposed NAPSSAF is derived by minimizing the l1-norm of the subband a posteriori error vector rather than the overall a posteriori error vector, which fully uses the subband adaptive filter’s inherent decorrelating property. Simulations in context of the system identification and acoustic echo cancellation (AEC) are carried out to demonstrate the advantages of the proposed algorithms. The results of simulations demonstrate that the proposed NAPSSAF obtains faster convergence rate than the existing algorithms.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Haykin S (2002) Adaptive filter theory. Prentice-Hall, Englewood Cliffs, NJ
Lee KA, Gan WS, Kuo SM (2009) Subband adaptive filter: theory and implementation. Wiley, Chichester, UK
Lee KA, Gan WS (2004) Improving convergence of the NLMS algorithm using constrained subband updates. IEEE Signal Process Lett 11(9):736–739
Seo JH, Park PG (2014) Variable individual step-size subband adaptive filtering algorithm. Electron Lett 50(3):177–178
Yu Y, Zhao H, Chen B (2016) A new normalized subband adaptive filter algorithm with individual variable step sizes. Circ Syst Signal Process 35(4):1407–1418
Ni J, Li F (2010) A variable step-size matrix normalized subband adaptive filter. IEEE Trans Audio Speech Lang Process 18(6):1290–1299
Mathews VJ, Cho SH (1987) Improved convergence analysis of stochastic gradient adaptive filters using the sign algorithm. IEEE Trans Acoust Speech Signal Process 35(4):450–454
Cho SH, Kim SD, Kim SS (1997) A modified adaptive sign algorithm used on the hybrid norm error criterion. In: Proceedings of the 40th Midwest symposium on circuits and systems, vol 2. Issue 2, pp 1346–1349
Shao T, Zheng YR, Benesty J (2010) An affine projection sign algorithm robust against impulsive interferences. IEEE Signal Process Lett 17(4):327–330
Ni J, Li F (2010) Variable regularisation parameter sign subband adaptive filter. Electron Lett 46(24):1605–1607
Kim JH, Chang JH, Nam SW (2013) Sign subband adaptive filter with l1-norm minimisation-based variable step-size. Electron Lett 49(21):1325–1326
Shin JW, Yoo JW, Park PG (2013) Variable step-size sign subband adaptive filter. IEEE Signal Process Lett 20(2):173–176
Yoo JW, Shin JW, Park PG (2014) A band-dependent variable step-size sign subband adaptive filter. Signal Process 104:407–411
Ni J, Chen X, Yang J (2014) Two variants of the sign subband adaptive filter with improved convergence rate. Signal Process 96:325–331
Zhao H, Zheng Z Wang Z, Chen B (2017) Improved affine projection subband adaptive filter for high background noise environments. Signal Process 137:356–362
Yu Y, Zhao H (2016) Novel sign subband adaptive filter algorithms with individual weighting factors. Sig Process 122:14–23
Yu Y, Zhao H (2017) Novel combination schemes of individual weighting factors sign subband adaptive filter algorithm. Int J Adapt Control Signal Process. https://doi.org/10.1002/acs.2755
Acknowledgements
This work was partially supported by National Science Foundation of P.R. China (Grant: 61571374, 61271340 and 61433011).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, Q., Zhao, H. (2018). Novel Affine Projection Sign Subband Adaptive Filter. In: Jia, L., Qin, Y., Suo, J., Feng, J., Diao, L., An, M. (eds) Proceedings of the 3rd International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2017. EITRT 2017. Lecture Notes in Electrical Engineering, vol 482. Springer, Singapore. https://doi.org/10.1007/978-981-10-7986-3_67
Download citation
DOI: https://doi.org/10.1007/978-981-10-7986-3_67
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7985-6
Online ISBN: 978-981-10-7986-3
eBook Packages: EnergyEnergy (R0)