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In view of the large computational complexity and poor real-time performance of  l 1 norm convex optimization process for DOA estimation based on compressed sensing sparse reconstruction, the SAMP greedy algorithm is used to replace the  l 1 norm to obtain the approximate solution of  l 0 norm optimization in DOA estimation. The array manifold matrix is divided by equal sinusoidal, which satisfies the MIP criterion better. An iterative regularization sparsity adaptive matching pursuit (IR-SAMP) algorithm is proposed, which makes the SAMP algorithm better approximate the signal sparsity and reduces the estimation error. IR-SAMP algorithm uses regularization method and backtracking screening, and eliminates the inappropriate atoms in the backtracking stage, so as to better approximate the signal source sparsity. Simulation results show that the proposed IR-SAMP algorithm in single snapshot DOA estimation under equal sinusoidal sparse division is better. At the same time, IR-SAMP algorithm has low computational complexity, as for coherent signal source, which also has a better estimation effect.

Equal Sinusoidal Division of Array Manifold Matrix Based Direction-of-Arrival Estimation for Dual-functional Radar-communication