Energy-Guided Multitarget Detection and Localization for Distributed MIMO Radar: Analysis and Solution

Abstract

This article investigates the multitarget detection and localization problem for distributed multiple-input multiple-output radar. We first derive the optimal approach of this issue in the high-dimensional space according to the generalized likelihood ratio test (GLRT) and the maximum likelihood estimation. Moreover, the theoretical multitarget localization accuracy under the considered discrete time signal model is analyzed by invoking the Cram$\acute{\text{e}}$r–Rao lower bound and the sampling lower bound. To deal with the high complexity problem of the optimal solution, an energy-guided framework including two modules is proposed to detect and locate multiple targets. In the first module, the energy accumulation approach based on the GLRT is employed for multitarget detection, and a fast energy accumulation strategy in terms of the far-field case is developed to improve the computational efficiency. In the second module, an innovative iterative similarity evaluation method is designed to determine the positions of multiple targets by exploiting the relationship between the energy accumulation characteristics and the multitarget existence conditions. The effectiveness of the proposed framework is verified via extensive simulations in both far-field and near-field cases.