一种基于深度学习的PIR多人定位方法
A Deep-learning-based Method for PIR-based Multi-person Localization
摘要：
基于热释电红外（PIR）传感器的无设备定位（DFL）因其低成本、低功耗、隐私保护等优点而备受关注。然而，大多数现有的基于PIR的DFL方法需要高部署密度来实现高定位精度。最近，一些工作提出，可以通过深入分析PIR传感器的模拟输出来降低部署密度。然而，这些方法还不能很好地处理多人场景下的定位任务。在本文中，我们提出了一种新的基于PIR的多人定位神经网络，它适当地利用了一系列的领域知识。具体而言，建议的网络包括两个模块：一个是用于确定的人数，另一个是用于确定他们的位置。同时，将人数统计模块进一步设计为两级网络：一级用于信号分离，另一级用于单人检测。定位模块也被设计成两级网络：一级用于信号提取，另一级用于单人定位。通过上述方法，在保持定位精度的前提下，将传统PIR方法的部署密度降低了约76%.
英文摘要：
Device-free localization (DFL) based on pyroelectric infrared (PIR) sensors has attracted much attention due to its advantages of low cost, low power consumption, and privacy protection. However, most existing PIR-based DFL methods require high deployment density to achieve high localization accuracy. Recently, a few works proposed that the deployment density can be reduced by deeply analyzing the analog output of PIR sensors. However, these methods can not well handle the localization task in multi-person scenarios yet. In this paper, we propose a novel neural network for PIR-based multi-person localization, which appropriately leverages a series of domain knowledge. Specifically, the proposed network consists of two modules: one is for determining the number of persons and another is for determining their locations. Meanwhile, the module of person counting is further designed as a two-stage network: one stage is for signal separation and another is for single-person detection. The module for localization is also designed as a two-stage network: one stage is for signal extraction and another is for single-person localization. Through the above methods, we succeed to remarkably reduce the deployment density of the traditional PIR-based method by about 76\%, while maintaining the localization accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
