Vector-Sensor Array DOA Estimation Based on Spatio-Temporal Correlation Matrices Joint Diagonalization Using Jacobi Rotation

In array signal processing, conventional Direction-of-arrival (DOA) estimation makes utilize of a pressure-sensor array to locate closely spaced sources impinging from different directions in the presence of considerable noise or interference. Recently, the vector-sensor array processing, which measures both pressure and particle velocity of the acoustic field at a point in space, has drawn much attention. In this paper, we develop a new approach which exploits the spatio-temporal correlation information of the underlying vector-sensor array signal to achieve better DOA estimation performance even in a noisy and coherent environment with few snapshots. Our proposed algorithm can efficiently combine all of the relevant spatio-temporal correlation matrices by the joint diagonalization approach, such as Jacobi rotation, to reduce the effect of noise and achieve the desired angular resolution. Simulation results show that significant improvements are achieved by our proposed approach under several frequently encountered scenarios, such as a single source and two closely spaced coherent sources. In addition, the statistical performance analysis illustrates the superior root mean square error (RMSE) performance of our proposed algorithm against noise.