SIAM Journal on Imaging Sciences, Volume 14, Issue 1, Page 26-57, January 2021.
This paper introduces a premier and innovative (real-time) multi-scale method for target classification in electrosensing. The intent is that of mimicking the behavior of the weakly electric fish, which is able to retrieve much more information about the target by approaching it. The method is based on a family of transform-invariant shape descriptors computed from generalized polarization tensors (GPTs) reconstructed at multiple scales. The evidence provided by the different descriptors at each scale is fused using Dempster--Shafer theory. Numerical simulations show that the recognition algorithm we propose performs undoubtedly well and yields a robust classification.

中文翻译：

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多尺度电子传感分类

SIAM影像科学杂志，第14卷，第1期，第26-57页，2021年1月。
本文介绍了一种主要且创新的（实时）多尺度方法，用于电子传感中的目标分类。目的是模仿弱电鱼的行为，该鱼能够通过接近目标来检索有关目标的更多信息。该方法基于一系列变换不变形状描述符，这些描述符是根据在多个尺度上重构的广义极化张量（GPT）计算出来的。使用Dempster-Shafer理论融合了每个尺度上不同描述符提供的证据。数值模拟表明，我们提出的识别算法无疑具有良好的性能，并且产生了鲁棒的分类。