Space–Time Adaptive Processing Using Deep Neural Network-Based Shrinkage Algorithm Under Small Training Samples

Abstract

This correspondence discusses a space-time adaptive processing (STAP) method using deep neural network (DNN) and shrinkage algorithm (SA) under small training samples. A DNN-based SA is developed to estimate the clutter-plus-noise covariance matrix (CNCM) from the sample covariance matrix. This technique establishes a learning-based nonlinear mapping between the sample eigenvalues and the optimal shrunk eigenvalues and thus provides an accurate CNCM estimation. Specifically, a DNN consisting of cascaded convolutional neural network (CNN) and fully connected network (FCN) is designed, where the CNN performs the feature extraction and the FCN derives the estimation of the shrunk eigenvalues. To improve the CNN performance, a deep residual learning strategy is introduced into CNN. Due to the lack of labeled data, an airborne radar system is simulated for the construction of dataset consisting of sample eigenvalues and optimal shrunk eigenvalues. Numerical results demonstrate that the proposed DNN-based SA STAP has superior performance in comparison with the state-of-the-art SA STAP methods.