A Novel Approach for Optimal Number of Antenna Elements for 1D-DOA Based on Compressive Sensing Framework

Over the past few decades, DOA estimation from the viewpoint of sparse signal has attracted a great deal of interest; the sparse recovery problem is linked to the compressive sensing (CS) reconstruction. In CS, one of the most well-known greedy algorithm is CoSaMP. The empirical performance of CoSaMP is very appealing, and convergence is with guarantee. here in this work, we propose a novel modified CoSaMP approach for DOA estimation which utilizes a threshold-based strategy to enable the dimension of search signal vector to be chosen in an adaptive manner during each iteration. When compared to conventional CS methods, the proposed method simulations showed improved sparse signal recovery.