Multi-Step Knowledge-Aided Iterative MUSIC for Direction Finding using Nested Arrays

In this work, we propose a subspace-based algorithm for direction-of-arrival (DOA) estimation using nested sensor arrays, referred to as multi-step knowledge-aided iterative nested MUSIC method (MS-KAI-MUSIC). Differently from existing knowledge-aided methods applied to uniform linear arrays (ULAs), which make use of available known DOAs to improve the estimation of the covariance matrix of the input data, the proposed MS-KAI-MUSIC employs knowledge of the structure of the augmented sample covariance matrix, which is also obtained by exploiting a difference co-array structure, and the gradual incorporation of prior knowledge, which is obtained on line. Simulations show that MS-KAI-MUSIC significantly outperforms existing techniques.