Single snapshot DOA estimation by compressive sampling

Direction-of-arrival (DOA) estimation has attracted a lot of attention on variety of research areas. A lot of high-resolution DOA estimation algorithms has been proposed, including MUSIC and ESPRIT. Most of these methods are subspace-based and won not work when the snapshot number is limited. In this paper, a high-resolution method with single snapshot is proposed basing on compressive sampling theory. The echoes are sparsely recovered in spatial domain, and high-resolution DOA estimation can be described with a underdetermined equation solving problem. Three algorithms, including diagonal loading least squares, ℓ1 Regularization, and Orthogonal Matching Pursuit (OMP), are adopt to solve the problem. In comparison with MUSIC, the novel method have several advantages: Firstly, it can be applied in the single snapshot scenario. Secondly, it does not estimate source number. Thirdly, it works well when the sources is coherent. Fourthly, it also works when the sensor number is less than source number. Simulation results show that compressive sampling methods can estimate the DOA accurately, especially for large array scenario.

► A high-resolution method with single snapshot is proposed basing on compressive sampling theory, including diagonal loading least squares, L1 Regularization, and Orthogonal Matching Pursuit.
► The method does not need to estimate the source number.
► It works well when the sources is coherent.
► It also works when the sensor number is less than source number.