RSSI和PC-CSI加权融合的指纹定位方法
Weighted fusion fingerprint localization based on RSSI and PC-CSI
刘方家 1廖子俊 2张赫航 1韩静瑶3
作者信息
- 1. 北京科技大学 机械工程学院,北京 100083;北京科技大学 顺德创新学院,广东 佛山 528300
- 2. 华中科技大学 电子信息与通信学院,武汉 430074
- 3. 北京科技大学 计算机与通信工程学院,北京 100083
- 折叠
摘要
针对基于RSSI和CSI的指纹定位技术易受环境干扰、定位精度较低的问题,提出了一种基于RSSI指纹和相位修正信道状态信息(phase correct based channel state information,PC-CSI)指纹的加权融合指纹定位技术.基于PC-CSI的指纹定位在传统基于CSI幅值的指纹定位基础上增加相位信息对定位结果进行修正,之后对RSSI指纹和PC-CSI指纹的定位结果加权重定位.实验结果表明,提出的加权融合指纹定位算法与基于CSI的主动定位算法相比,平均定位误差(mean position error,MPE)降低了 36.2%,能满足室内定位需求.
Abstract
To meet the demand for indoor positioning,WiFi-based indoor positioning technologies have emerged,mainly including fingerprint positioning technologies based on received signal strength indicator(RSSI)and channel state informa-tion(CSI).The existing RSSI and CSI-based fingerprint localization techniques are susceptible to environmental interfer-ence and have low localization accuracy.In this paper,we propose a weighted fusion fingerprint localization technique based on RSSI fingerprints and phase correct based channel state information(PC-CSI)fingerprints.The PC-CSI fingerprint lo-calization adds phase information to the traditional CSI amplitude-based fingerprint localization to correct the localization re-sults,and then the localization results of RSSI fingerprint and PC-CSI fingerprint are weighted and repositioned.Experimen-tal findings demonstrate a 36.2%reduction in mean position error(MPE)compared to CSI-based active localization meth-ods,showcasing the efficacy of our proposed approach in meeting indoor positioning requirements.
关键词
室内定位技术/接收信号强度指示(RSSI)/信道状态信息(CSI)/加权K近邻(WKNN)算法Key words
indoor positioning techniques/received signal strength indicator(RSSI)/channel state information(CSI)/weight-K-nearest neighbor(WKNN)引用本文复制引用
基金项目
广东省普通高校特色创新项目(2022WTSCX315)
出版年
2024
国家科技期刊平台
NSTL
万方数据