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A Wireless LAN Location Estimation System Using Center of Gravity as an Algorithm Selector for Enhancing Location Estimation

机译:使用重心作为增强位置估计的算法选择器的无线局域网位置估计系统

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摘要

With the prevalence of mobile Wi-Fi devices and infrastructures, there are growing interests in mobile surveillance and device tracking for providing better location-aware services in metropolitan areas. With a good location estimation integrated into a wireless infrastructure, system administrators can closely monitor the network traffic as well as the behavior of the mobile users. The Received Signal Strength(RSS), easily available information from Access Point(AP) Sensors, has become the most popular research approach. However, in reality received signal strength is affected by factors such as occlusion, signal deflections and reflections. There had been proposed estimation systems that use the Fingerprinting approach to provide good and accurate location recommendation. But such systems have been drawn back by their time-intensive training and retraining process. The solution to Signal Strength-based estimation, therefore, is to devise a system that minimizes the training Andre adaptation process while attaining good accuracy in location estimation. This paper proposes a location estimation system whose estimation method is based on the Center of Gravity(CG)method. This method also serves as an algorithm selector such that the system can switch to another estimation algorithm if need be. The aim of this system is to reduce the high cost of training and re-calibration but attain an accuracy comparable to the Fingerprinting location estimation approach.
机译:随着移动Wi-Fi设备和基础设施的普及,人们越来越关注移动监视和设备跟踪,以在都市圈中提供更好的位置感知服务。通过将良好的位置估计功能集成到无线基础架构中,系统管理员可以密切监视网络流量以及移动用户的行为。接收信号强度(RSS)是可从接入点(AP)传感器轻松获得的信息,已成为最受欢迎的研究方法。但是,实际上,接收到的信号强度受诸如阻塞,信号偏转和反射等因素的影响。已经提出了使用指纹方法来提供良好且准确的位置推荐的估计系统。但是,此类系统因其耗时的培训和再培训过程而退缩。因此,基于信号强度的估计的解决方案是设计一种系统,该系统可以最小化训练Andre适应过程,同时在位置估计中获得良好的准确性。提出了一种基于重心法的位置估计系统。该方法还用作算法选择器,以便系统可以根据需要切换到另一种估计算法。该系统的目的是减少培训和重新校准的高成本,但要达到与指纹位置估计方法相当的精度。

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