Multi-step knowledge-aided iterative ESPRIT for direction finding

摘要

In this work, we propose a subspace-based algorithm for direction-of-arrival (DOA) estimation, referred to as multi-step knowledge-aided iterative estimation of signal parameters via rotational invariance techniques (ESPRIT) method (Multi-Step KAI-ESPRIT), which achieves more accurate estimates than those of prior art. Differently from current knowledge aided methods, which make use of available known DOAs to improve the estimation of the covariance matrix of the input data, the proposed Multi-Step KAI-ESPRIT exploits knowledge of the structure of the covariance matrix and its perturbation terms and the gradual embodiment of the prior knowledge, which is obtained on line. Simulation results illustrate the improvement achieved by the proposed method.