Weak Moving Target Detection in Distributed MIMO Radar With Hybrid Data

In this article, we investigate the problem of point-like weak moving target detection in the distributed multiple-input multiple-output (MIMO) radar. Due to limitations in communication bandwidth, data from certain spatially diversity channels (SDCs) necessitate low-bit quantization before transmission to the fusion center, while high-precision echo data from other channels can be utilized directly. The transmission channels are modeled as binary symmetric channels. Given the challenge in obtaining the maximum likelihood estimate of the target complex reflection coefficient, we initially develop a fusion detector applicable to the hybrid data using the Rao criterion, where its asymptotic distribution is also analyzed. Simultaneously, optimizing quantization thresholds for the low-bit quantized SDC using Fisher information as the objective function is conducted to maximize the detection probability. As a crucial aspect of the moving target detection, we examine the problem of target velocity estimate, presenting a maximum likelihood estimator for target velocity and deriving a closed-form Cramér–Rao lower bound. Finally, two metrics are designed to characterize the impact of various factors on the performance of the distributed MIMO radar, named as normalized detection probability gain and normalized velocity estimation precision gain, respectively. Simulation results indicate that under high-quality transmission channels, 2-bit quantization yields performance close to optimal. In scenarios of poor channel quality, high-precision echo data help to alleviate the adverse effects of low-bit data on signal fusion when channel distortion occurs.