Low complexity approximate cyclic adaptive matching pursuit
We present an improved Adaptive Matching Pursuit algorithm for computing approximate sparse solutions for overdetermined systems of equations. The algorithms use a greedy approach, based on a neighbor permutation, to select the ordered support positions followed by a cyclical optimization of the selected coefficients. The sparsity level of the solution is estimated on-line using Information Theoretic Criteria. The performance of the algorithm approaches that of the sparsity informed RLS, while the complexity remains lower than that of competing methods.