This paper presents a novel approach to the localization of moving targets in a complex environment based on the measurement of the perturbations induced by the target presence on an independently‐generated time‐varying electromagnetic field. Field perturbations are measured via a set of sensors deployed over the domain of interest and used to detect and track a possible target by resorting to a particle Bernoulli filter (PBF). To comply with real‐time operation, the PBF works along with an artificial neural network (ANN) model of the environment trained offline via finite elements (FEs). The performance of the proposed algorithm is assessed via simulation experiments.

中文翻译：

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通过电磁场测量进行被动目标检测和跟踪

本文基于在独立产生的时变电磁场上测量目标存在引起的扰动，提出了一种在复杂环境中定位运动目标的新颖方法。场扰动是通过部署在感兴趣域上的一组传感器进行测量的，并通过求助于粒子伯努利滤波器（PBF）来检测和跟踪可能的目标。为了遵守实时操作，PBF与通过有限元（FE）离线训练的环境的人工神经网络（ANN）模型一起工作。通过仿真实验评估了所提出算法的性能。