机载雷达深度展开空时自适应处理方法

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。 

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

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.