Report on Two-Step Knowledge-Aided Iterative ESPRIT Algorithm
Incremental improvement for DOA estimation in signal processing, offering better accuracy for applications like radar and communications.
The paper proposes a two-step knowledge-aided iterative ESPRIT algorithm for DOA estimation that improves covariance matrix estimation by incorporating prior knowledge, achieving more accurate estimates than prior art.
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.