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Direction of arrival (DOA) estimation from sparse signal representation has gained much attention in recent years. In this, the spatial signal is reconstructed by using a Compressive sensing (CS) framework. CS is a new paradigm by which the signal acquisition and reconstruction are carried out at sub-Nyquist rates. The limitation of the Nyquist sampling theorem is overcome by sparse sampling and reconstruction. This paper uses the CS framework in DOA estimation to reduce the underlying computational cost in the reconstruction process. Many reconstruction algorithms have been described in the past years. However, the comparative study on the reconstruction performances for CS-based DOA estimation is lacking. This work primarily concentrates on different reconstruction algorithms that are utilized in CS. The performance of various reconstruction algorithms for single snapshot DOA estimation is compared in this paper. Different parameters, like finding the target failure rate, Root Mean Square Error (RMSE), execution time are considered to evaluate the performance of the techniques.

Performance Comparison of Reconstruction Algorithms in Compressive Sensing Based Single Snapshot DOA Estimation