Linear phase sparse system identification in the presence of impulsive noise

ABSTRACT In this paper, we incorporate an ℓp-norm sparsity constraint into linear phase constrained affine projection-sign algorithm (LPCAP-SA). The proposed algorithm combines the effect of linear phase constrained affine projection sign algorithm and ℓp-norm sparsity constraint .The proposed algorithm has better performance for linear phase sparse system identification in the presence of vigorous impulsive interference. The proposed ℓp-norm LPCAP-SA is based on the ℓ1-norm minimisation criterion on posterior error and ℓp-Norm on weight coefficients and a linear phase constraint. Simulations in Matlab software show that the proposed ℓp-norm LPCAP-SA has robustness against impulsive interferences and sparse environment for linear phase system.

[1]  Arumugam Sivabalan,et al.  ZA-APA with zero attractor controller selection criterion for sparse system identification , 2018, Signal Image Video Process..

[2]  Kazuhiko Ozeki.  Theory of Affine Projection Algorithms for Adaptive Filtering , 2015 .

[3]  M. T. Schiavoni,et al.  A linearly constrained minimization approach to adaptive linear phase and notch filters , 1988, [1988] Proceedings. The Twentieth Southeastern Symposium on System Theory.

[4]  Jacob Benesty,et al.  An Affine Projection Sign Algorithm Robust Against Impulsive Interferences , 2010, IEEE Signal Processing Letters.

[5]  J. Chambers,et al.  A robust mixed-norm adaptive filter algorithm , 1997, IEEE Signal Processing Letters.

[6]  José Antonio Apolinário,et al.  Constrained normalized adaptive filters for CDMA mobile communications , 1998, 9th European Signal Processing Conference (EUSIPCO 1998).

[7]  Fumiyuki Adachi,et al.  Variable-step-size based sparse adaptive filtering algorithm for channel estimation in broadband wireless communication systems , 2014, EURASIP J. Wirel. Commun. Netw..

[8]  F. Y. Wu,et al.  Non-Uniform Norm Constraint LMS Algorithm for Sparse System Identification , 2013, IEEE Communications Letters.

[9]  José Antonio Apolinário,et al.  THE CONSTRAINED AFFINE PROJECTION ALGORITHM — DEVELOPMENT AND CONVERGENCE ISSUES , 1999 .

[10]  V. Nascimento,et al.  Sparsity-aware affine projection adaptive algorithms for system identification , 2011 .

[11]  O. L. Frost,et al.  An algorithm for linearly constrained adaptive array processing , 1972 .

[12]  A. A. Beex,et al.  Convergence behavior of affine projection algorithms , 2000, IEEE Trans. Signal Process..

[13]  João Marcos Travassos Romano,et al.  Simplified FLS algorithm for linear phase adaptive filtering , 1998, 9th European Signal Processing Conference (EUSIPCO 1998).

[14]  Wentao Ma,et al.  Sparse least mean p-power algorithms for channel estimation in the presence of impulsive noise , 2016, Signal Image Video Process..

[15]  Yanyan Wang,et al.  Sparse-aware set-membership NLMS algorithms and their application for sparse channel estimation and echo cancelation , 2016 .

[16]  Vahid Tarokh,et al.  SPARLS: The Sparse RLS Algorithm , 2010, IEEE Transactions on Signal Processing.

[17]  E. Erzin,et al.  Adaptive filtering for non-Gaussian stable processes , 1994, IEEE Signal Processing Letters.

[18]  Arumugam Sivabalan,et al.  Steady-State Analysis of Sparsity-Aware Affine Projection Sign Algorithm for Impulsive Environment , 2017, Circuits Syst. Signal Process..