Two Dimensional Direction-of-Arrival Estimation Using Compressive Measurements

Abstract

Compressive sensing can effectively reduce the dimension of a signal vector and has been widely used in many areas. Direction-of-arrival (DOA) estimation using compressive measurements has gained considerable attention due to the reduction in system complexity. In this paper, we extend this structure into two-dimensional (2D) case, where the azimuth and elevation angles are considered. A uniform rectangular array is used as the receive array. Similar to the one-dimensional case, the received signals are compressed before fed into the front-end circuit chains. Then, a DOA estimation algorithm, referred to as 2D compressive measurement-based multiple signal classification is proposed for the proposed scheme, where the azimuth and elevation angles are estimated jointly. The proposed scheme can obtain a higher estimation accuracy than the classical 2D DOA estimation structure when the number of front-end circuit chains keeps the same. On the other hand, when the number of sensors in the receive array is the same, the proposed scheme can dramatically reduce the system complexity and cost by compressing the dimension of the received signal vector. The effectiveness of the proposed scheme is validated by numerical simulations.