PDF(Pubmed)
Abstract：
Millimeter-wave (mmWave) radars attain high resolution without compromising privacy while being unaffected by environmental factors such as rain, dust, and fog. This study explores the challenges of using mmWave radars for the simultaneous detection of people and small animals, a critical concern in applications like indoor wireless energy transfer systems. This work proposes innovative methodologies for enhancing detection accuracy and overcoming the inherent difficulties posed by differences in target size and volume. In particular, we explore two distinct positioning scenarios that involve up to four mmWave radars in an indoor environment to detect and track both humans and small animals. We compare the outcomes achieved through the implementation of three distinct data-fusion methods. It was shown that using a single radar without the application of a tracking algorithm resulted in a sensitivity of 46.1%. However, this sensitivity significantly increased to 97.10% upon utilizing four radars using with the optimal fusion method and tracking. This improvement highlights the effectiveness of employing multiple radars together with data fusion techniques, significantly enhancing sensitivity and reliability in target detection.
摘要：
毫米波（mmWave）雷达在不影响隐私的情况下获得高分辨率，同时不受雨水等环境因素的影响，灰尘,和雾。这项研究探讨了使用毫米波雷达同时检测人和小动物的挑战，在室内无线能量传输系统等应用中的一个关键问题。这项工作提出了创新的方法，以提高检测精度并克服目标尺寸和体积差异带来的固有困难。特别是,我们探索了两种不同的定位场景，涉及室内环境中多达四个毫米波雷达，以检测和跟踪人类和小动物。我们比较了通过实施三种不同的数据融合方法获得的结果。结果表明，在不应用跟踪算法的情况下使用单个雷达的灵敏度为46.1％。然而,在使用最佳融合方法和跟踪的四个雷达时，该灵敏度显着提高到97.10％。这种改进突出了将多个雷达与数据融合技术一起使用的有效性，显著提高目标检测的灵敏度和可靠性。
