In Spectrum cartography (SC), the generation of exposure maps for radio frequency electromagnetic fields (RF-EMF) spans dimensions of frequency, space, and time, which relies on a sparse collection of sensor data, posing a challenging ill-posed inverse problem. Cartography methods based on models integrate designed priors, such as sparsity and low-rank structures, to refine the solution of this inverse problem. In our previous work, EMF exposure map reconstruction was achieved by Generative Adversarial Networks (GANs) where physical laws or structural constraints were employed as a prior, but they require a large amount of labeled data or simulated full maps for training to produce efficient results. In this paper, we present a method to reconstruct EMF exposure maps using only the generator network in GANs which does not require explicit training, thus overcoming the limitations of GANs, such as using reference full exposure maps. This approach uses a prior from sensor data as Local Image Prior (LIP) captured by deep convolutional generative networks independent of learning the network parameters from images in an urban environment. Experimental results show that, even when only sparse sensor data are available, our method can produce accurate estimates.

翻译：摘要：在频谱制图（SC）中，射频电磁场（RF-EMF）暴露图的生成跨越频率、空间和时间维度，这依赖于稀疏的传感器数据采集，构成了一个不适定的逆问题。基于模型的制图方法通过整合设计先验（如稀疏性和低秩结构）来优化该逆问题的求解。在我们先前的工作中，利用生成对抗网络（GANs）实现了EMF暴露图重建，其中采用了物理定律或结构约束作为先验，但这类方法需要大量标注数据或模拟完整地图进行训练才能产生有效结果。本文提出一种仅利用GANs中生成器网络的重建EMF暴露图方法，该方法无需显式训练，从而克服了GANs需要参考完整暴露图等局限性。该方法是利用传感器数据先验作为局部图像先验（LIP），由深度卷积生成网络在无需从城市环境图像中学习网络参数的情况下捕获获得。实验结果表明，即便仅能获取稀疏传感器数据，我们的方法仍能生成准确的估计结果。