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.
作者
朱晗归
冯为可
冯存前
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)
出处
《雷达学报（中英文）》
EI
CSCD
北大核心
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)。
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
Space-Time Adaptive Processing(STAP)
Sparse recovery
Deep learning
Deep unfolding/unrolling(DU)
Array error
