Multi-Step Knowledge-Aided Iterative ESPRIT for Direction Finding

TL;DR

This paper introduces a novel iterative ESPRIT algorithm that leverages prior knowledge and data covariance refinement to improve direction-of-arrival estimation, especially for closely-spaced sources.

Contribution

It presents a new multi-step, knowledge-aided iterative ESPRIT method that enhances DOA estimation accuracy by reducing covariance matrix disturbances and incorporating online prior knowledge.

Findings

Improved DOA estimation accuracy for closely-spaced sources.

Reduced mean squared error of the data covariance matrix.

Demonstrated computational efficiency compared to existing algorithms.

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.