On Maximum Likelihood Angle of Arrival Estimation Using Orthogonal Projections

Abstract

We present a novel and efficient approach for estimating the maximum likelihood (ML) estimates of the angles-of-arrival (AoAs) of multiple sources. The approach is iterative and is based on orthogonal projections in order to optimise the ML cost function, thus the name OPML. As will be shown, the advantage of using an orthogonal basis of the signal manifold would allow solving the ML cost function in an iterative manner. In fact, we propose two algorithms based on OPML, i.e. OPML-1 and OPML-2, which exhibit lower computational complexity and faster convergence than existing ML algorithms. In this paper, we discuss the idea of OPML and its two implementations, followed by simulation results to demonstrate their performance.