机载雷达深度展开空时自适应处理方法  

Deep Unfolding Based Space-Time Adaptive Processing Method for Airborne Radar

作　　者：朱晗归 冯为可 冯存前[1] 邹帛 路复宇 ZHU Hangui;FENG Weike;FENG Cunqian;ZOU Bo;LU Fuyu(Air and Missile Defense College,Air Force Engineering University,Xi’an 710051,China)

机构地区：[1]空军工程大学防空反导学院,西安710051

出　　处：《雷达学报（中英文）》2022年第4期676-691,共16页Journal of Radars

基　　金：国家自然科学基金(62001507),陕西省高校科协青年人才托举计划(20210106)。

摘　　要：稀疏恢复空时自适应处理(SR-STAP)方法能够利用少量训练距离单元实现对机载雷达杂波的有效抑制。然而,现有SR-STAP方法均基于模型驱动实现,存在着参数设置困难、运算复杂度高等问题。针对这些问题,该文将基于模型驱动的SR方法和基于数据驱动的深度学习方法相结合,首次将深度展开(DU)引入到机载雷达杂波抑制和目标检测之中。首先,建立了阵列误差(AE)条件下的杂波空时谱和阵列误差参数联合估计模型,并利用交替方向乘子法(ADMM)进行求解;接着,将ADMM算法展开为深度神经网络AE-ADMM-Net,利用充足完备的数据集对其迭代参数进行优化;最后,利用训练后的AE-ADMM-Net对训练距离单元数据进行处理,快速获得杂波空时谱和阵列误差参数的准确估计。仿真结果表明:与典型SR-STAP方法相比,该文所提出的DU-STAP方法能够在保持较低运算复杂度的同时提高杂波抑制性能。The Sparse Recovery Space-Time Adaptive Processing(SR-STAP)method can use a small number of training range cells to effectively suppress the clutter of airborne radar.The SR-STAP approach may successfully eliminate airborne radar clutter using a limited number of training range cells.However,present SR-STAP approaches are all model-driven,limiting their practical applicability due to parameter adjustment difficulties and high computational cost.To address these problems,this study,for the first time,introduces the Deep Unfolding/Unrolling(DU)method to airborne radar clutter reduction and target recognition by merging the model-driven SR method and the data-driven deep learning method.Firstly,a combined estimation model for clutter space-time spectrum and Array Error(AE)parameters is established and solved using the Alternating Direction Method of Multipliers(ADMM)algorithm.Secondly,the ADMM algorithm is unfolded to a deep neural network,named AE-ADMM-Net,to optimize all iteration parameters using a complete training dataset.Finally,the training range cell data is processed by the trained AE-ADMM-Net,jointly estimating the clutter space-time spectrum and the radar AE parameters efficiently and accurately.Simulation results show that the proposed DU-STAP method can achieve higher clutter suppression performance with lower computational cost compared to typical SR-STAP methods.

关 键 词：空时自适应处理 稀疏恢复 深度学习 深度展开 阵列误差 

分 类 号：V221.3[航空宇航科学与技术—飞行器设计] TN951[电子电信—信号与信息处理]