Complex ADMM-Net, a complex-valued neural network architecture inspired by the alternating direction method of multipliers (ADMM), is designed for interference removal in stepped-frequency radar super-resolution angle-range-doppler imaging. We consider an uncooperative spectrum sharing scenario where the radar is tasked with imaging a sparse scene amidst communication interference that is frequency-sparse due to spectrum underutilization, motivating an $\ell _1$ -minimization problem to recover the radar image and suppress the interference. The problem's ADMM iteration undergirds the neural network design, yielding a set of generalized ADMM updates with learnable hyperparameters and operations. The network is trained with random data generated according to the radar and communication signal models. In numerical experiments ADMM-Net exhibits markedly lower error and computational cost than ADMM and CVX.

中文翻译：

---

ADMM-Net用于步进频率雷达中的通信干扰消除

Complex ADMM-Net 是一种受乘法器交替方向法 (ADMM) 启发的复值神经网络架构，旨在消除步进频率雷达超分辨率角距多普勒成像中的干扰。我们考虑一个不合作的频谱共享场景，其中雷达的任务是在由于频谱未充分利用而频率稀疏的通信干扰中对稀疏场景进行成像，激发$\ell _1$ - 最小化问题以恢复雷达图像并抑制干扰。该问题的 ADMM 迭代巩固了神经网络设计，产生了一组具有可学习超参数和操作的广义 ADMM 更新。该网络使用根据雷达和通信信号模型生成的随机数据进行训练。在数值实验中，ADMM-Net 的误差和计算成本明显低于 ADMM 和 CVX。