Enhanced Single-Snapshot 1-D and 2-D DOA Estimation Using Particle Swarm Optimization

Abstract

In radar applications, target range, velocity (Doppler), and angle are the three primary measurements employed. Estimating the number of targets and their directions of arrival (DOAs) on the antenna array can be achieved through various methods. Conventional techniques such as the correlation and Multiple Signal Classification (MUSIC) algorithms offer a straightforward approach for DOA estimation. However, these methods necessitate an exhaustive search of the entire spectrum and require numerous temporal snapshots to accurately identify the spatial spectrum peaks. To address this challenge, Particle Swarm Optimization (PSO)-correlation and PSO-MUSIC methods have been proposed. These PSO-based techniques provide a systematic approach to locate the spatial spectrum peak in both one-dimensional (1-D) and two-dimensional (2-D) scenarios. In order to determine the exact target position, the global best particle location is iteratively updated by these methods. The statistical performance of the 1-D and 2-D PSO-correlation and PSO-MUSIC algorithms demonstrates that these techniques exhibit higher accuracy in comparison to existing single-snapshot DOA estimation methods. The estimation performance of the proposed algorithms is analyzed and justified by employing the Cramé r-Rao bound (CRB).