Performance Degradation of DOA Estimation in Distributed Radar Networks Under Near-Field Influence

Abstract

In striving for optimal performance in distributed radar networks tailored for short-range applications, conventional direction-of-arrival (DOA) estimation often proves inadequate. The presence of close-in targets introduces a mismatch in the radar echo model, challenging the validity of far-field (FF) assumptions. To address this problem, we have developed a misspecified Cramér-Rao bound (MCRB) for DOA estimation in distributed radar networks influenced by near-field (NF) effects. The derivation aids in understanding potential performance degradations associated with the mean-squared error (mse) of a misspecified maximum-likelihood estimator. Through comprehensive analysis, we explore the interaction between the usual Cramér-Rao bound (CRB) and the MCRB. Moreover, we conduct a meticulous investigation into the relationship between these bounds, target parameters, and system architecture. Our examination significantly advances radar performance in practical scenarios, providing valuable insights to inform the design and configuration of distributed radar systems.