Phase Transition Analysis for Compressive Sensing Based DoA Estimation in Automotive Radar

In automotive radar applications, the compressive sensing (CS) based DoA estimation is used in array signal processing in recent years. Sparse reconstruction has the potential to estimate the direction of arrival (DoA) with super resolution. However, failed results may be acquired via sparse reconstruction in inappropriate conditions, namely the critical condition determining success or failure must be taken into consideration. In this paper, the sparsity of the scenario and the signal-to-noise ratio (SNR) are analyzed as the main factors via phase transition diagrams. Other factors affecting the success or failure are also investigated, such as the array configuration and the sparse recovery algorithm. Simulated and experimental results demonstrate the critical conditions, in which the DoA estimation is successful or failed.