Abstract
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) [1] and statistically efficient Modified Variable Projection (MVP) [2] algorithms to arbitrary sensor array configurations without explicit knowledge of the steering vector. The proposed algorithms exploit the concept of Manifold Separation Technique (MST) [3], [4]. 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).
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