The prediction of the 2-D electric field strength distribution from a limited set of measurements and without any prior information on the source is addressed in this letter. Towards this aim, an innovative sparseness-promoting approach is presented based on the profitable integration of a total-variation compressive sensing (TV-CS) recovery technique with the LOLA Voronoi adaptive sampling strategy. From the one hand, sparsity priors are enforced on the discrete gradient of the unknown field strength allowing to exploit physical knowledge on the addressed problem. On the other hand, the LOLA Voronoi approach enables a reduction of the number of field samples and to properly build the TV-CS observation operator. Representative numerical results are discussed to assess, also comparatively, the potentialities and features of the proposed method.

中文翻译：

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通过自适应全变 CS 技术进行可靠的场强预测

这封信解决了从一组有限的测量中预测二维电场强度分布的问题，并且没有关于源的任何先验信息。为了实现这一目标，基于全变差压缩感知 (TV-CS) 恢复技术与 LOLA Voronoi 自适应采样策略的有利集成，提出了一种创新的稀疏促进方法。一方面，在未知场强的离散梯度上强制执行稀疏先验，从而可以利用所解决问题的物理知识。另一方面，LOLA Voronoi 方法可以减少现场样本的数量并正确构建 TV-CS 观测算子。讨论了具有代表性的数值结果，以比较地评估所提出方法的潜力和特征。