High-Accuracy DOA Estimation of Coherent Signals Based on the Time-Space Mutual Correlation Smoothing

Abstract

To solve the problem of low estimation accuracy of spatial smoothing algorithm in scenarios of low signal-to-noise ratio (SNR) and small number of snapshots, a high-accuracy direction of arrival (DOA) estimation algorithm of coherent signals based on the time-space mutual correlation smoothing (TSMCS) is proposed in this letter. First, according to the strong correlation of the signal and the weak correlation of the noise in time and space domains, the time-space mutual correlation matrix of different sub-arrays is constructed to suppress the noise. Secondly, a set of high-order covariance matrices is obtained by multiplying all time-space mutual correlation matrices by their respective conjugate transpose, followed by spatial smoothing. Subsequently, TSMCS covariance matrices are meticulously decomposed and reconstructed, resulting in a more comprehensive matrix. Then, the particle swarm optimization (PSO) algorithm is exploited to optimize the signal subspace by adjusting the delay value. Finally, the traditional TLS-estimation signal parameters via the rotational invariance technique (ESPRIT) algorithm are employed to estimate the DOA of coherent signals. Simulation results demonstrate that the proposed algorithm has higher estimation accuracy and resolution.