Interpretable STAP Algorithm Based on Deep Convolutional Neural Network

In practical settings, the efficacy of Space-Time Adaptive Processing (STAP) algorithms relies on acquiring sufficient Independent Identically Distributed (IID) samples. However, sparse recovery STAP method encounters challenges like model parameter dependence and high computational complexity. Furthermore, current deep learning STAP methods lack interpretability, posing significant hurdles in debugging and practical applications for the network. In response to these challenges, this paper introduces an innovative method: a Multi-module Deep Convolutional Neural Network (MDCNN). This network blends data- and model-driven techniques to precisely estimate clutter covariance matrices, particularly in scenarios where training samples are limited. MDCNN is built based on four key modules: mapping, data, priori and hyperparameter modules. The front- and back-end mapping modules manage the pre- and post-processing of data, respectively. During each equivalent iteration, a group of data and priori modules collaborate. The core network is formed by multiple groups of these two modules, enabling multiple equivalent iterative optimizations. Further, the hyperparameter module adjusts the trainable parameters in equivalent iterations. These modules are developed with precise mathematical expressions and practical interpretations, remarkably improving the network’s interpretability. Performance evaluation using real data demonstrates that our proposed method slightly outperforms existing small-sample STAP methods in nonhomogeneous clutter environments while significantly reducing computational time.