Abstract:
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 covar...Show MoreMetadata
Abstract:
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.
Published in: IEEE Transactions on Aerospace and Electronic Systems ( Volume: 54, Issue: 5, October 2018)