# Report on Two-Step Knowledge-Aided Iterative ESPRIT Algorithm

## TL;DR

This paper introduces a two-step iterative ESPRIT algorithm that leverages prior knowledge and covariance matrix structure to enhance the accuracy of DOA estimation in signal processing.

## Contribution

It presents a novel two-step knowledge-aided iterative ESPRIT method that improves DOA estimation accuracy by incorporating prior information and covariance matrix structure.

## Key findings

- Improved DOA estimation accuracy over prior methods
- Effective utilization of prior knowledge in covariance matrix estimation
- Simulation results demonstrate superior performance

## Abstract

In this work, we propose a subspace-based algorithm for direction-of-arrival (DOA) estimation, referred to as two-step knowledge-aided iterative estimation of signal parameters via rotational invariance techniques (ESPRIT) method (Two-Step KAI-ESPRIT), which achieves more accurate estimates than those of prior art. The proposed Two-Step KAI-ESPRIT improves the estimation of the covariance matrix of the input data by incorporating prior knowledge of signals and by exploiting knowledge of the structure of the covariance matrix and its perturbation terms. Simulation results illustrate the improvement achieved by the proposed method.