Multi-Step Knowledge-Aided Iterative ESPRIT for Direction Finding
Signal Processing
2018-05-02 v1 Machine Learning
Machine Learning
Abstract
In this work, we propose a subspace-based algorithm for DOA estimation which 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 MSE 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.
Cite
@article{arxiv.1805.00169,
title = {Multi-Step Knowledge-Aided Iterative ESPRIT for Direction Finding},
author = {S. F. B. Pinto and R. C. de Lamare},
journal= {arXiv preprint arXiv:1805.00169},
year = {2018}
}
Comments
7 figures. arXiv admin note: text overlap with arXiv:1703.10523