Arithmetical Analysis of WSN based Indoor Positioning Localization Systems with Kalman Filtering

R. SambathKumar, S. Gowshameed, S. Arunmozhi
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引用次数: 2

Abstract

(WSN) is used for determining the Indoor Positioning of objects and persons in recent years. WSN has been implemented in indoor positioning applications such as real-time tracking of humans/objects, patient monitoring in health care, navigation, warehouses for inventory monitoring, shopping malls, etc. But one of the problems while implementing WSN an Indoor positioning system is to ensure more coverage large number of sensors must be deployed which increases the installation cost. So, in this paper, MATLAB GUI named Sensor Network Localization Explorer to analyze the impact of node density on indoor aligning localization schemes. Later Kalman filter with the indoor positioning system to increase the reliability and reduce localization error of the system is introduced with a lesser number of nodes.
基于卡尔曼滤波的WSN室内定位系统的算法分析
近年来,无线传感器网络(WSN)被用于确定室内物体和人的定位。WSN已经在室内定位应用中实现,例如人/物体的实时跟踪,医疗保健中的患者监控,导航,库存监控仓库,购物中心等。但实现WSN室内定位系统的问题之一是为了保证更大的覆盖范围,必须部署大量的传感器,从而增加了安装成本。因此,本文采用MATLAB图形化界面Sensor Network Localization Explorer来分析节点密度对室内对准定位方案的影响。随后在室内定位系统中引入卡尔曼滤波,以减少节点数量,提高系统的可靠性,减少系统的定位误差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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