Method for Sparse Representation of Complex Data Based on Overcomplete Basis, l1 Norm, and Neural MFNN-like Network

Abstract

The article presents the results of research into a method for representing complex data based on an overcomplete basis and l0/l1 norms. The proposed method is an extended modification of the neural-like MFNN (minimum fuel neural network) for the case of complex data. The influence of the choice of activation function on the performance of the method is analyzed. The results of the numerical simulation demonstrate the effectiveness of the proposed method for the case of sparse representation of complex data and can be used to determine the direction of arrival (DOA) for a uniform linear array (ULA).