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Multistep Knowledge-Aided Iterative ESPRIT: Design and Analysis

2018; Institute of Electrical and Electronics Engineers; Volume: 54; Issue: 5 Linguagem: Inglês

10.1109/taes.2018.2811098

2371-9877

Silvio F. B. Pinto, Rodrigo C. de Lamare,

Indoor and Outdoor Localization Technologies

In this work, we propose a subspace-based algorithm for direction-of-arrival (DOA) estimation that iteratively reduces the disturbance factors of the estimated data covariance matrix and incorporates prior knowledge which is gradually obtained on line. An analysis of the mean squared error of the reshaped data covariance matrix is carried out along with comparisons between computational complexities of the proposed and existing algorithms. Simulations focusing on closely-spaced sources, where they are uncorrelated and correlated, illustrate the improvements achieved.