Adaptive Distributed MIMO Radar Waveform Optimization Based on Mutual Information

A novel approach to optimizing the waveforms of an adaptive distributed multiple-input multiple-output (MIMO) radar is developed. The research work aims at improving the target detection and feature extraction performance by maximizing the mutual information (MI) between the target impulse response and the received echoes in the first step, and then minimizing the MI between successive backscatter signals in the second step. These two stages correspond to the design of the ensemble of excitations and the selection of a suitable signal out of the ensemble, respectively. The waveform optimization algorithm is based upon adaptive learning from the radar scene, which is achieved through a feedback loop from the receiver to the transmitter. This feedback includes vital information about the target features derived from the reflected pulses. In this way the transmitter adjusts its probing signals to suit the dynamically changing environment. Simulation results demonstrate better target response extraction using the proposed two-step algorithm as compared with each single-step optimization method. This approach also results in improved target detection probability and delay-Doppler resolution as the number of iterations increases.