Multi-Target Counting and Localization in Distributed MIMO Radar Based on Energy Modeling and Compressive Sensing

Target localization is one of the most important research topics in the field of radar signal processing. In this paper, the problem of multi-target counting and localization in the distributed multiple-input multiple-output (MIMO) radar is investigated. We first analyze the theoretical bound of the multi-target localization accuracy in the discrete time signal model. It is determined by the Cramer-Rao lower bound (CRLB) at low signal-to-noise ratio (SNR) and the sampling lower bound (SLB) when the SNR is high. Furthermore, an innovative multi-target counting and localization scheme is developed, which is based on the energy modeling of the multiple transmitter-receiver paths and the compressive sensing theory. To solve the sparse vector recovery issue, we design a lightweight iterative greedy pursuit algorithm including the similarity evaluation strategy. The proposal utilizes the samples of the raw signals and belongs to the category of the direct localization. Nevertheless, it has significantly higher computational efficiency and lower communication burden than the conventional direct localization methods, while avoids the complex data association that encountered by the indirect localization methods. Finally, the simulation results validate the effectiveness and robustness of the proposed method.