Adaptive 2-D DOA Estimation using Subspace Fitting

Direction-of-arrival (DOA) estimation is a ubiquitous task in array processing. In this paper, we propose an adaptive 2-dimensional direction finding framework to track multiple moving targets by using the subspace fitting method. First, we expand the steering vectors of the current snapshot in a Taylor series around the DOAs of the previous snapshot. Then we transform the subspace fitting problem into a set of linear equations. As a result, the DOAs of each snapshot can be updated by solving a set of linear equations and we no longer need to search the 2-D spatial spectrum. In comparison with the traditional 2-D MUSIC, the proposed method not only reduces the computational complexity considerably but also has better estimation performance.

1. Introduction

Direction-of-arrival (DOA) estimation is a ubiquitous task concerned in array processing, which has been widely used in wireless communication, radar, sonar, acoustics, astronomy, medical imaging, and other areas. In this paper, both the azimuth and elevation angles are of interest and we assume that they are time-varying.