论文标题
用于跨频率雷达中沟通干扰的ADMM网络
ADMM-Net for Communication Interference Removal in Stepped-Frequency Radar
论文作者
论文摘要
复杂的ADMM-NET是一种受乘数交替方向方法（ADMM）启发的复杂评估神经网络架构，设计用于在超分辨率的超分辨率跨频率雷达角度范围范围内形成式成像中的干扰去除。量身定制的，其中MIMO雷达与通信共享频谱，ADMM-NET恢复了雷达图像 - 假定该图像稀疏----同时消除了通信干扰，由于频谱的不足，这在频率域中被稀疏建模为稀疏。该方案激发了一个$ \ ell_1 $ - 最大化问题，其ADMM迭代又涉及神经网络设计，产生了一组具有可学习的超参数和操作的广义ADMM迭代。为了训练网络，我们使用根据雷达和通信信号模型生成的随机数据。在数值实验中，ADM-NET的误差和计算成本明显低于ADMM和CVX。
Complex ADMM-Net, a complex-valued neural network architecture inspired by the alternating direction method of multipliers (ADMM), is designed for interference removal in super-resolution stepped frequency radar angle-range-doppler imaging. Tailored to an uncooperative scenario wherein a MIMO radar shares spectrum with communications, the ADMM-Net recovers the radar image---which is assumed to be sparse---and simultaneously removes the communication interference, which is modeled as sparse in the frequency domain owing to spectrum underutilization. The scenario motivates an $\ell_1$-minimization problem whose ADMM iteration, in turn, undergirds the neural network design, yielding a set of generalized ADMM iterations that have learnable hyperparameters and operations. To train the network we use 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.
