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In this work, we propose a subspace-based algorithm for direction-of-arrival(DOA) estimation applied to the signals impinging on a two-level nested array,referred to as multi-step knowledge-aided iterative nested MUSIC method(MS-KAI-Nested-MUSIC), which significantly improves the accuracy of theoriginal Nested-MUSIC. Differently from existing knowledge-aided methodsapplied to uniform linear arrays (ULAs), which make use of available known DOAsto improve the estimation of the covariance matrix of the input data, theproposed Multi-Step KAI-Nested-MU employs knowledge of the structure of theaugmented sample covariance matrix, which is obtained by exploiting thedifference co-array structure covariance matrix, and its perturbation terms andthe gradual incorporation of prior knowledge, which is obtained on line. Theeffectiveness of the proposed technique can be noticed by simulations focusingon uncorrelated closely-spaced sources.

Study of Multi-Step Knowledge-Aided Iterative Nested MUSIC for Direction Finding