Comparison of Sparse Representation Methods for Complex Data Based on the Smoothed L0 Norm and Modified Minimum Fuel Neural Network

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This article outlines the results of comparison methods for representing complex data based on a redundant basis using the L0 norm and analyses the method of a modified MFNN (minimum fuel neural network) and the sparse representation method for the complex-data SL0 (smoothed L0 norm), based on the smoothed L0 norm. The example of numerical modeling for determining the direction of arrival (DOA) of sources received by an equidistant antenna array (ULA—Uniform Linear Array) shows that the SL0 method ensures a high convergence rate. However, unlike the MFNN-like neural network method, it does not guarantee convergence to the correct solution.