Vector-sensor array direction-of-arrival estimation exploiting spatial time-frequency structure based on joint approximate diagonalization

By making use of the extra particle velocity information, an array of vector sensors can achieve better Direction-of-arrival (DOA) estimation performance than a conventional array of pressure sensors. However, it is noted that most of the previous work on DOA estimation with vector-sensor array uses only the time-space statistical information available on the array signals and does not exploit the difference in the time-frequency signatures of the sources. In this paper, we develop a new approach which exploits the inherent time-frequency-space characteristics of the underlying vector- sensor array signal to achieve better DOA estimation performance even in a noisy and coherent environment with few snapshots. It turns out that our approach is based on the spatial time-frequency distributions (STFD) information and can efficiently combine all of the relevant STFD points by the joint approximate diagonalization approach, such as Jacobi rotation, to reduce the effect of noise and achieve the desired angular resolution. Computer simulations with several frequently encountered scenarios, such as multiple closely spaced coherent sources, indicate the superior DOA estimation resolution of our proposed approach as compared with existing techniques. In addition, from a statistical point of view, the performance of our proposed approach is investigated more closely by considering the root mean square error (RMSE) respectively versus SNRs, snapshots, or number of sensors and its excellent performance for higher DOA estimation accuracy is demonstrated.