AoA Estimation Using Sufficient Statistic-Based MUSIC Algorithms for Low SNR

This letter primarily focuses on efficient angular estimation in low signal-to-noise ratio (SNR) conditions, as well as small-sample scenarios, aiming to facilitate the application of Integrated Sensing and Communication (ISAC) techniques. We propose a novel algorithm derived from multiple signal classification (MUSIC), called SS-MUSIC, which incorporates the sufficient statistic theorem. Simulation results demonstrate that SS-MUSIC achieves lower estimation errors in low-SNR conditions compared with existing MUSIC-based approaches, without increased complexity, and also exhibits superior performance in multi-target and small-sample scenarios.