Rapid advancements in communication technologies in the Internet of Things (IoT) domain have had an impact on the application of positioning technology across multiple domains. Although there have been numerous fully fledged approaches for detection and localization in outdoor scenarios, due to high path loss and shadowing, these are insufficiently accurate in indoor scenarios. The primary enabler of various healthcare and safety applications is the precise sensing and localization of targets. A cost-effective approach with little maintenance is crucial for the development of such reliable systems. To address such sensing and localization challenges in indoor scenarios, we propose a novel dynamic target detection technique based on an ensembled convolutional neural network (CNN) classifier. An AWR1843 Radar sensor is used to collect data corresponding to dynamic targets in indoor scenarios. The range of each moving target in the room is estimated using point cloud data extracted from the received signal. An ensemble-based 1-D CNN classifier is used to analyze the data. To model the ensemble classifier, we used three CNN classifiers. The performances of the state-of-the-art classifiers considered in the comparison varied between 44 $\%$ and 95 $\%$ in terms of accuracy. In contrast, the proposed system attained an accuracy of 97.65 $\%$ during training and 96.47 $\%$ during testing and outperformed the state-of-the-art approaches.

中文翻译：

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利用毫米波雷达传感器和集成机器学习进行动态目标占用状态检测

物联网（IoT）领域通信技术的快速进步对定位技术跨领域的应用产生了影响。尽管已经有许多成熟的室外场景检测和定位方法，但由于高路径损耗和阴影，这些方法在室内场景中不够准确。各种医疗保健和安全应用的主要推动力是目标的精确感测和定位。对于开发这种可靠的系统来说，具有成本效益且几乎不需要维护的方法至关重要。为了解决室内场景中的此类传感和定位挑战，我们提出了一种基于集成卷积神经网络（CNN）分类器的新型动态目标检测技术。 AWR1843雷达传感器用于收集室内场景中动态目标对应的数据。使用从接收信号中提取的点云数据来估计房间中每个移动目标的范围。使用基于集成的一维 CNN 分类器来分析数据。为了对集成分类器进行建模，我们使用了三个 CNN 分类器。比较中考虑的最先进分类器的性能在 44 $\%$和 95 $\%$在准确性方面。相比之下，所提出的系统的准确度为 97.65 $\%$训练期间和 96.47 $\%$在测试过程中，其性能优于最先进的方法。