DOA estimation of the quasi‐stationary signal using sparse reconstruction

Abstract

Given the problem of the direction of arrival (DOA) estimation of quasi-stationary signals, a sparse reconstruction algorithm based on the sparse array is proposed in this study to improve the DOA estimation performance. Specifically, the quasi-stationary signal is modelled based on the interleaved array (IA), where the algorithm makes full use of the high degree of freedom (DOF) and large array aperture of the interleaved array in the virtual domain. Then, the angle parameters are estimated depending on sparse reconstruction and the Khatri-Rao transform, which can achieve a higher number of DOF. Compared with the angle measurement algorithm based on uniform linear array and nested linear array, the algorithm proposed in this study not only increases the DOF of angle measurement significantly, but also improves the accuracy of angle measurement. Finally, numerical simulations demonstrate the validity of the proposed method.

DOA Estimation of Quasi-stationary Signals Based on a Separated Generalized Nested Array

“…However, some non-stationary signals whose statistical properties can remain stable for a certain period are called quasi-stationary signals. At present, there are many methods to estimate the DOAs for quasi-stationary signals, the most common being Khatri-Rao multiple signal classification (KR-MUSIC) [4], tensor modeling [5], and sparse reconstruction [6].…”