%I Elsevier
%V 92
%T Iterative reweighted l1 design of sparse FIR filters
%P 905-911
%N 4
%J Signal Processing
%R 10.1016/j.sigpro.2011.09.031
%X Sparse FIR filters have lower implementation complexity than full filters, while keeping a good performance level. This paper describes a new method for designing 1D and 2D sparse filters in the minimax sense using a mixture of reweighted l1 minimization and greedy iterations. The combination proves to be quite efficient; after the reweighted l1 minimization stage introduces zero coefficients in bulk, a small number of greedy iterations serve to eliminate a few extra coefficients. Experimental results and a comparison with the latest methods show that the proposed method performs very well both in the running speed and in the quality of the solutions obtained.
%L eprints1529
%D 2012
%A Cristian Rusu
%A Bogdan Dumitrescu
%K Sparse filters; Reweighted l1 minimization; Greedy algorithms