Sliding window greedy RLS for sparse filters

We present a sliding window RLS for sparse filters, based on the greedy least squares algorithm. The algorithm adapts a partial QR factorization with pivoting, using a simplified search of the filter support that relies on a neighbor permutation technique. For relatively small window size, the proposed algorithm has a lower complexity than recent exponential window RLS algorithms. Time-varying FIR channel identification simulations show that the proposed algorithm can also give better mean squared coefficient errors.