Low complexity azimuth and elevation estimation for arbitrary array configurations

In this paper we propose azimuth and elevation angle of arrival estimation algorithms for arbitrary array configurations. The proposed algorithms extend the Polynomial Rooting Intersection for Multidimensional Estimation (PRIME) and statistically efficient Modified Variable Projection (MVP) algorithms to arbitrary sensor array configurations without explicit knowledge of the steering vector. The proposed algorithms exploit the concept of Manifold Separation Technique (MST). Thus, the data are processed in the element-space domain and are not subject to mapping errors. Moreover, closed-form derivatives of the Weighted Subspace Fitting (WSF) cost function are obtained, even for real-world arrays with imperfections, making the proposed MVP computationally attractive. The obtained estimates for both elevation and azimuth show an error variance close to the Cramer-Rao Lower Bound (CRLB).