Zhaolong Wang;Xiaokuan Zhang;Weike Feng;Xixi Chen;Ninghui Li
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引用次数: 0

摘要

作为机载雷达的一种有效杂波抑制方法,基于原子规范最小化(ANM)的时空自适应处理(STAP)方法存在计算复杂度高和参数设置困难的问题。为了解决这些问题,本研究为 STAP 提出了一种深度展开(DU)ANM 算法。首先,建立了基于 ANM 的杂波估计问题。然后,通过交替方向乘法(ADMMs)和深度神经网络(DNN)解决该问题,DNN 通过设计适当的损失函数和构建完整的数据集进行训练。最后,通过训练有素的网络处理训练范围内的单元数据,得到杂波加噪声协方差矩阵(CNCM)和 STAP 加权向量。仿真结果表明,与现有的 ANM-STAP 方法相比,所提出的 DU-ANM-STAP 方法能以更低的计算成本实现更高的杂波和噪声抑制性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Unfolded Atomic Norm Minimization Algorithm for Space-Time Adaptive Processing
As an effective clutter suppression method for airborne radar, the atomic norm minimization (ANM)-based space-time adaptive processing (STAP) method suffers from high computational complexity and parameter setting difficulty. To solve these problems, a deep unfolded (DU) ANM algorithm is proposed for STAP in this study. First, the clutter estimation problem based on ANM is established. Then, the problem is solved via the alternating direction method of multipliers (ADMMs) and a deep neural network (DNN), which is trained by designing an appropriate loss function and constructing a complete dataset. At last, the clutter-plus-noise covariance matrix (CNCM) and the STAP weighting vector are obtained by processing the training range cell data via the trained network. Simulation results show that the proposed DU-ANM-STAP method can achieve higher clutter and noise suppression performance with lower computational cost than the existing ANM-STAP methods.
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