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首页> 外文期刊>Wireless personal communications: An Internaional Journal >ERLAK: On the Cooperative Estimation of the Real-Time RSSI Based Location and K Constant Term
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ERLAK: On the Cooperative Estimation of the Real-Time RSSI Based Location and K Constant Term

机译:ERLAK:基于RSSI的实时位置和K恒定术语的合作估计

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摘要

Nowadays location estimation using WiFi networks in indoor environments has become a hot research topic. Challenging methods without calibration or hardware integration are essentially required for cost-effective and practical solutions. The Received Signal Strength Indicator-based localization methods offer low cost solutions. However, their propagation models are difficult to characterize due to environmental factors in indoor and multiple parameters. There are a number of works over estimation of location and pathloss exponent presented in the literature. This paper introduces a new method shortly named as ERLAK in order to estimate the K constant term using log normal channel model in addition to the location of mobile station in indoor environment. The ERLAK method has been consistently compared to the well-known Least Square and Weighted Least Square methods. It achieves the least errors in distance estimations compared to the classical methods on especially critical measurement points. It remarkably accomplishes less than 5 m mean errors for distance estimation results particularly when signal is received from all of the access points.
机译:如今使用室内环境中的WiFi网络的位置估计已成为一个热门的研究主题。没有校准或硬件集成的具有挑战性的方法基本上是具有成本效益和实用的解决方案。基于信号强度指示符的本地化方法提供低成本解决方案。然而,由于室内和多个参数中的环境因素,它们的传播模型难以表征。在文献中介绍的位置和路径阶段有许多作品。本文介绍了一种新的方法,不久被命名为Erlak,以便使用日志正常通道模型来估计K常项术语除了移动台在室内环境中的位置。与众所周知的最小正方形和加权最小二乘法相比,ERLAK方法一致。与尤其是临界测量点的经典方法相比,距离估计中的最小误差达到了距离估计。它特别地完成了距离估计结果的误差,特别是当从所有接入点接收信号时,距离估计结果小于5米的误差。

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