High Performance Sparse Forward-Looking Imaging of Distributed Millimeter-Wave Radar

High performance and high resolution single snapshot imaging technology is one of the keys to empowering radar development. For millimeter-wave radar performance to meet the stringent requirements of forward-looking imaging, conventional processing of a single sensor by straightforward scaling of the radar parameters is often restricted. Here, this paper presents an effective and high accuracy settlement for forward-looking imaging acquisition in spatial distributed radar system. Firstly, we construct a joint angle-range complex signal model for multiple-input multiple-output scheme, and propose the joint Bayesian matching pursuit (jBMP) algorithm to employ sparse sensing-based estimation of unsynchronized nodes. Our primary goal is to find a global minimum mean squared error (MMSE) estimator involving Bernoulli-Gaussian variables, and to adopt the general linear fusion rule for the distributed Bayesian structure. The uniqueness of the solution lies in the fact that the associated support set obtained through greedy search is globally shared by all nodes. Finally, a series of simulation and real data obtained from the distributed radar systems is presented to illustrate the validity and robustness of the proposed approach.