MIMO Radar Sparse Recovery Imaging with Wideband Interference Prediction

Multiple-input multiple-output (MIMO) radar three-dimensional (3D) imaging is widely applied in military and civil fields. However, MIMO easily affected by wideband interference (WBI). To solve this problem, study, we propose a sparse recovery method with WBI prediction based on the predictive recurrent neural network (PredRNN) tensor-based smooth L0 (TSL0) algorithm. Firstly, extract time-frequency (TF) feature of historical measured via short-time Fourier transform (STFT) operation. In way, can use PredRNN to exploit spatiotemporal correlation TF domain predict future. Then, adaptively design random stepped frequency waveform selecting non-overlapped frequencies according predicted feature. Finally, apply TSL0 algorithm reconstruct 3D high-resolution target image from signal cube. Simulation results show high performance robustness proposed presence different WBIs.