A Learned Ambiguity-Depression Method for Forward-Looking Radar With Perturbed Antenna Array

This letter presents a novel method to resolve the ambiguity in forward-looking radar systems. Traditional methods for ambiguity suppression face challenges with antenna deviations and the ill-conditioning of measurement matrices. To address these limitations, we introduce a deviation-adaptive unfolding network (DAUNet), which integrates perturbed compressive sensing (PCS) and advanced deep learning techniques. The DAUNet efficiently handles the space variance of the measurement matrix by utilizing matrix direct summation and the Kronecker product and modeling the ambiguity suppression as a unified PCS problem. This method incorporates iterative learning processes and a novel neural network architecture to reconstruct ambiguity-free images from multichannel forward-looking radars. Simulation results demonstrate that our approach significantly outperforms the existing methods in handling both point targets and distributed scenarios.