CLT-NN MUSIC for Robust DoA Estimation: Distinguishing Eigenvalue Differences Between Signal and Noise

The estimation of the signal source number presents a key challenge for the multiple signal classification (MUSIC) algorithm under low signal-to-noise ratio (SNR) conditions. However, previous methods have difficulty addressing low SNR scenarios. To solve this problem, this work proposes a CLT-NN MUSIC method, which provides a new strategy to distinguish between signal and noise eigenvalues. Specifically, based on the Central Limit Theorem (CLT), we establish the eigenvalue perturbation distribution model for the sample covariance matrix and derive the K-estimation criterion. Subsequently, we adopt neural networks (NN) to fit the aforementioned perturbation model. Through a dynamic decision mechanism, this approach enables the adaptive estimation of the number of sources under low SNR. Simulation results show that CLT-NN MUSIC significantly improves the estimation performance even under low SNR.