Adaptive 2-D DOA Estimation using Subspace Fitting
Jie Zhuang, Yang Lu, Guo-Yong Ning, Ishak Ali Hussein, Wei Wang · 2018
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.