Deep Unfolded Atomic Norm Minimization Algorithm for Space-Time Adaptive Processing

Abstract

As an effective clutter suppression method for airborne radar, the atomic norm minimization (ANM)-based space-time adaptive processing (STAP) method suffers from high computational complexity and parameter setting difficulty. To solve these problems, a deep unfolded (DU) ANM algorithm is proposed for STAP in this study. First, the clutter estimation problem based on ANM is established. Then, the problem is solved via the alternating direction method of multipliers (ADMMs) and a deep neural network (DNN), which is trained by designing an appropriate loss function and constructing a complete dataset. At last, the clutter-plus-noise covariance matrix (CNCM) and the STAP weighting vector are obtained by processing the training range cell data via the trained network. Simulation results show that the proposed DU-ANM-STAP method can achieve higher clutter and noise suppression performance with lower computational cost than the existing ANM-STAP methods.