Sparse adaptive filtering by iterative hard thresholding

In this paper, we present a new algorithm for sparse adaptive filtering, drawing from the ideas of a greedy compressed sensing recovery technique called the iterative hard thresholding (IHT) and the concepts of affine projection. While usage of affine projection makes it robust against colored input, the use of IHT provides a remarkable improvement in convergence speed over the existing sparse adaptive algorithms. Further, the gains in performance are achieved with very little increase in computational complexity.