基于可解释深度卷积网络的空时自适应处理方法

Interpretable STAP Algorithm Based on Deep Convolutional Neural Network

在实际应用中,空时自适应处理(STAP)算法的性能受限于足够多独立同分布(IID)样本的获取。然而,目前可有效减少IID样本需求的算法仍面临一些问题。针对这些问题,该文融合数据驱动和模型驱动思想,构建了具有明确数学含义的多模块深度卷积神经网络(MDCNN),实现了小样本条件下对杂波协方差矩阵快速、准确、稳定估计。所构建MDCNN网络由映射模块、数据模块、先验模块和超参数模块组成。其中,前后端映射模块分别对应数据的预处理和后处理;单组数据模块和先验模块共同完成一次迭代优化,网络主体由多组数据模块和先验模块构成,可实现多次等效迭代优化;超参数模块则用来调整等效迭代中可训练参数。上述子模块均具有明确数学表述和物理含义,因此所构造网络具有良好的可解释性。实测数据处理结果表明,在实际非均匀杂波环境下该文所提方法杂波抑制性能优于现有典型小样本STAP方法,且运算时间较后者大幅降低。

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.