A low computational complexity normalized subband adaptive filter algorithm employing signed regressor of input signal

ᅟ In this paper, the signed regressor normalized subband adaptive filter (SR-NSAF) algorithm is proposed. This algorithm is optimized by L 1 -norm minimization criteria. The SR-NSAF has a fast convergence speed and a low steady-state error similar to the conventional NSAF. In addition, the proposed...

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Bibliographic Details
Published in:	EURASIP journal on advances in signal processing Vol. 2018; no. 1; pp. 1 - 23
Main Authors:	Shams Esfand Abadi, Mohammad, Shafiee, Mohammad Saeed, Zalaghi, Mehrdad
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 02-04-2018 Springer Springer Nature B.V SpringerOpen
Subjects:	Adaptive algorithms Adaptive filters Algorithms Analysis Complexity Computation Computer simulation Energy conservation Engineering Mean-square performance Nonstationary environments Normalized subband adaptive filter (NSAF) Quantum Information Technology Regression analysis Signal,Image and Speech Processing Signed regressor (SR)L 1-norm Spintronics Steady state System identification Mean-square performance Signed regressor (SR) Normalized subband adaptive filter (NSAF) norm
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Summary:	ᅟ In this paper, the signed regressor normalized subband adaptive filter (SR-NSAF) algorithm is proposed. This algorithm is optimized by L 1 -norm minimization criteria. The SR-NSAF has a fast convergence speed and a low steady-state error similar to the conventional NSAF. In addition, the proposed algorithm has lower computational complexity than NSAF due to the signed regressor of the input signal at each subband. The theoretical mean-square performance analysis of the proposed algorithm in the stationary and nonstationary environments is studied based on the energy conservation relation and the steady-state, the transient, and the stability bounds of the SR-NSAF are predicated by the closed form expressions. The good performance of SR-NSAF is demonstrated through several simulation results in system identification, acoustic echo cancelation (AEC) and line EC (LEC) applications. The theoretical relations are also verified by presenting various experimental results.
ISSN:	1687-6180 1687-6172 1687-6180
DOI:	10.1186/s13634-018-0542-z