DOA Estimation Based on a Sparsity Pre-estimation Adaptive Matching Pursuit Algorithm

The traditional subspace class algorithm has low or even no estimation accuracy for DOA estimation under the conditions of less number of snapshots, low SNR and source coherence. Therefore, the application of compressed sensing theory in DOA estimation is studied in this paper. To address the problems of poor estimation accuracy of sparsity adaptive matching Pursuit(SAMP) algorithm in noisy environment and the need to gradually approximate the true sparsity from zero, a sparsity pre-estimation adaptive matching Pursuit(SPAMP) algorithm is proposed in this paper . The algorithm in this paper optimizes the iterative termination conditions of the algorithm by using the changing rules of iterative residuals, and at the same time approximates the source sparsity quickly and accurately by pre-estimating the initial sparsity.. The simulation results show that the algorithm in this paper has the advantages of high estimation accuracy, fast operation speed and better noise immunity, which promotes further integration of compressed sensing and DOA estimation in practical situations.