一种基于视距路径识别的设备无关室内定位算法

Wi-Fi技术的广泛应用和部署催生了许多基于Wi-Fi的室内定位技术。近年来，基于Wi-Fi的设备无关定位算法引起了研究人员的广泛注意。设备无关定位算法不需要目标对象携带无线传输设备，而是通过测量目标对象对无线信号传输的影响来反向推断目标对象的位置。由于不需要目标对象携带相关设备，因此可以广泛应用于多种场合，如老人健康护理等。已有的设备无关定位技术通常需要事先采集训练数据，因此容易受室内复杂多变的环境干扰，导致定位精度下降。 提出一种基于视距路径检测的设备无关定位算法。利用物理层信道状态信息CSI，可以判断一对无线收发设备之间的路径是否是视距LoS路径。在此基础上，提出一个新的设备无关定位算法，该算法在监测区域部署一组Wi Fi收发装置，对任意一对无线设备，通过识别它们之间是否存在视距路径来判断目标对象是否在这对设备的菲涅耳区域内。此外，还提出一种基于投票的方法来获得目标对象的最可能位置。在实际设备上的实验结果表明，该定位算法可以达到0.5 m左右的精度，并且不需事先训练，具有较高的实时性。

A device-free indoor localization algorithm based on line-of-sight path identification

The widespread application and deployment of Wi-Fi technology has spawned many Wi-Fi based indoor positioning technologies. In recent years, Wi Fi based device-free positioning algorithm has attracted the attention of researchers. The device-free positioning algorithm does not require the target object to carry the wireless transmission device, but instead estimates the target object’s position by measuring the impact of the target object on the wireless signal transmission. Because it does not need the target to carry the relevant equipment, it can be widely used in many occasions such as elderly health care. Existing device-free positioning technologies usually require pre-acquisition of training data, so they are easily affected by the indoor complex and changeable environments to result in the degradation of localization accuracy. This paper proposes a device-free localization algorithm based on line-of-sight path detection. By using the Channel State Information (CSI), we can determine whether the path between a pair of wireless transceivers is a Line of Sight (LoS) path. On this basis, we propose a new device free positioning algorithm. The algorithm deploys a set of Wi-Fi transceivers in the monitoring area. For any pair of wireless devices, we determine whether the target object is within the Fresnel zone of the device by identifying whether there is a line-of-sight path between them or not. We propose a polling-based approach to get the most probable location of the target. The experimental results on the actual equipment show that the proposal can achieve an accuracy of about 0.5 meters, does not require prior training, and has higher real-time performance.