High-Resolution DOA Estimation Using Compressive Sensing with Deterministic Sensing Matrices and Compact Generalized Coprime Arrays

This paper proposes a new high-resolution Direction Of Arrival (DOA) estimation approach for generalized coprime antenna arrays using Compressive Sensing (CS) with chaotic deterministic sensing matrices. The performance is investigated for different antenna array configurations with and without CS using both Multiple Signal Classification (MUSIC) and Capon DOA estimation techniques. The performance is evaluated in terms of the spatial spectrum, the computational time, and the Root Mean Square Error (RMSE) between estimated and actual DOAs when changing the Signal to Noise Ratio (SNR), the number of snapshots, and the number of antenna array elements. Theoretical analysis and simulation results show that the use of CS with chaotic deterministic sensing matrices not only allows resolving very closed signal sources well with comparable performance when using the actual measurement vector, but also reduces significantly the computational time for high-resolution DOA estimation of generalized coprime antenna arrays.