Zero attraction penalty and attraction compensation combined sparse LMS method

Abstract

The invention relates to a zero attraction penalty and attraction compensation combined sparse LMS method, and belongs to the field of signal processing. According to the method, zero attraction penalty and attraction compensation are combined, coefficients of an estimation filter are divided into a near-zero coefficient, a small coefficient and a large coefficient, and then different attraction methods are adopted; in each iterative update, for estimating a near-zero coefficient of the filter, only a product term in an iterative update formula is used for calculation; then trace attraction compensation is carried out on the large coefficient of the estimation filter, so that the convergence speed of the coefficient of the estimation filter to approach the large coefficient of the channel is accelerated; for the small coefficient of the estimation filter, if the coefficient approaches the zero coefficient value of the channel or the large coefficient value of the channel in the iteration process, processing is carried out according to the method for estimating the near zero coefficient and the large coefficient of the filter, otherwise, simple zero attraction punishment is carried out on the coefficient. The method is high in convergence speed, low in complexity and wide in tuning parameter application range.

Description

technical field [0001] The invention belongs to the field of signal processing and relates to a sparse LMS method combining zero attraction penalty and attraction compensation. Background technique [0002] Many channels are sparse, and identifying such sparse channels requires specific adaptive filtering algorithms. At present, the types of algorithms for sparse system identification are l 0 -norm, l 1 -norm and l p -norm, where l 0 -norm is to perform zero-attraction penalties on the coefficients of the estimated filter within a certain small threshold, l 1 -norm is a zero-attraction penalty for all coefficients of the estimated filter, l p is a zero-attraction penalty involving division and exponents for all coefficients of the estimated filter. l p -norm achieved better than l 0 -norm and l 1 The -norm type algorithm has better performance, but due to the high complexity, this method is difficult to implement in hardware. l 1 The typical algorithm in the -norm...

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Problems solved by technology

l p -norm achieved better than l 0 -norm and l 1 The -norm type algorithm has better performance, but due to high complexity, this method is difficult to implement in hardware

l 1 The typical algorithm in the -norm type is Zero-attracting Least MeanSquare (ZA-LMS), which gives the same zero-attracting penalty to all channel coefficients, and does not distinguish between zero and non-zero channel coefficients, resulting in Its steady-state mean square deviation (Mean Square deviation, MSD) is not excellent

Y.Chen also proposed a reweighted ZA-LMS (Reweight Zero-attracting Least Mean Square, RZA-LMS). The zero-attracting function of this method cleve...

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