计算机与现代化2024,Issue(1) :35-40.DOI:10.3969/j.issn.1006-2475.2024.01.006

基于RSSI参数动态修正的ZigBee室内定位算法

ZigBee Indoor Location Algorithm Based on Dynamic Modification of RSSI Parameters

李世宝 丛玉杰
计算机与现代化2024,Issue(1) :35-40.DOI:10.3969/j.issn.1006-2475.2024.01.006

基于RSSI参数动态修正的ZigBee室内定位算法

ZigBee Indoor Location Algorithm Based on Dynamic Modification of RSSI Parameters

李世宝 1丛玉杰1
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作者信息

  • 1. 中国石油大学(华东)海洋与空间信息学院,山东 青岛 266580
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摘要

ZigBee室内定位技术近年来发展迅速,但使用固定路径损耗模型的传统算法环境适应能力较差,会引起较大定位误差,影响定位精度.本文提出一种基于ZigBee平台的对数路径损耗模型参数动态修正的室内定位算法.首先经过高斯滤波对所得RSSI值进行筛选优化,然后根据锚节点之间的距离以及RSSI值来动态修正对数路径损耗模型参数,包括路径损耗因子以及距待测节点处的信号强度值,从而得到当下环境中具体的对数路径损耗模型;再利用卡尔曼滤波对现有的定位参数进行二次修正,以更正上述算法中因时刻变动引起的环境变化导致的定位偏差.实验结果表明,该定位算法比基于ZigBee的固定路径损耗模型定位性能提升了46.8%,可以改善因环境变化产生的定位误差问题.

Abstract

ZigBee indoor positioning technology has developed rapidly in recent years,but the traditional algorithm using fixed path loss model has poor adaptability to the environment,resulting in large positioning errors and affecting positioning accuracy.This paper proposes an indoor location algorithm based on ZigBee platform with dynamic correction of logarithmic path loss model parameters.First,the RSSI value obtained is filtered and optimized by Gaussian filtering,and then the parameters of the logarith-mic path loss model are dynamically modified according to the distance between anchor nodes and the RSSI value,including the path loss factor and the signal strength value from the node to be measured,so the specific logarithmic path loss model in the cur-rent environment is obtained;Then the Kalman filter is used to modify the existing positioning parameters twice,which can cor-rect the positioning deviation caused by the environment change caused by the time change in the above algorithm.Experimental results show that the positioning performance of this algorithm is 46.8%higher than that of the fixed path loss model based on Zig-Bee,which can improve the positioning error caused by environmental changes.

关键词

室内定位/ZigBee/高斯滤波/对数路径损耗模型/卡尔曼滤波

Key words

indoor positioning/ZigBee/Gaussian filtering/logarithmic path loss model/Kalman filtering

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基金项目

国家自然科学基金-山东省联合基金资助项目(U1906217)

国家自然科学基金资助项目(61972417)

出版年

2024
计算机与现代化
江西省计算机学会 江西省计算技术研究所

计算机与现代化

CSTPCD
影响因子:0.472
ISSN:1006-2475
参考文献量12
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