Deception Parameter Estimation and Discrimination in Distributed Multiple-Radar Architectures

Abstract

This paper considers the problem of discriminating false targets generated by deception jamming jointly with the target localization in distributed multiple-radar architectures. A unified parameter estimation model is developed for both real targets and false targets with the under-estimate parameters as the target location and deception range. For a real target, the estimated location is just its physical location and the deception range is zero, whereas for a false target, the estimated location corresponds to the jammer location and the deception range is nonzero. Therefore, the deception range serves as the direct statistic in the target discrimination. The Cramer-Rao lower bound (CRLB) is derived to evaluate the estimation accuracy, and is shown to provide a tight bound in most the cases. With the estimation of the deception range and its CRLB, the optimal discrimination algorithm in the Neyman-Pearson sense is designed. The simulations evaluate the estimation accuracy under the generalized model, and the feasibility of the discrimination algorithm is verified.