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RSSI和PC-CSI加权融合的指纹定位方法
引用本文:刘方家,廖子俊,张赫航,韩静瑶. RSSI和PC-CSI加权融合的指纹定位方法[J]. 重庆邮电大学学报(自然科学版), 2024, 0(2): 328-336
作者姓名:刘方家  廖子俊  张赫航  韩静瑶
作者单位: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;华中科技大学 电子信息与通信学院, 武汉 430074;北京科技大学 计算机与通信工程学院, 北京 100083
基金项目:广东省普通高校特色创新项目(2022WTSCX315)
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。

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
收稿时间:2023-01-17
修稿时间:2023-10-13

Weighted fusion fingerprint localization based on RSSI and PC-CSI
LIU Fangji,LIAO Zijun,ZHANG Hehang,HAN Jingyao. Weighted fusion fingerprint localization based on RSSI and PC-CSI[J]. Journal of Chongqing University of Posts and Telecommunications, 2024, 0(2): 328-336
Authors:LIU Fangji  LIAO Zijun  ZHANG Hehang  HAN Jingyao
Affiliation:School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, P.R. China;Shunde Innovation School, University of Science and Technology Beijing, Foshan 528300, P.R. China;School of Electronic Information and Communication, Huazhong University of Science and Technology, Wuhan 430074, P.R. China; School of Computer & Communication Engineering, University of Science and Technology Beijing, Beijing 100083, P.R. China
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 information (CSI). The existing RSSI and CSI-based fingerprint localization techniques are susceptible to environmental interference 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 localization adds phase information to the traditional CSI amplitude-based fingerprint localization to correct the localization results, and then the localization results of RSSI fingerprint and PC-CSI fingerprint are weighted and repositioned. Experimental findings demonstrate a 36.2% reduction in mean position error (MPE) compared to CSI-based active localization methods, showcasing the efficacy of our proposed approach in meeting indoor positioning requirements.
Keywords:indoor positioning techniques  received signal strength indicator (RSSI)  channel state information (CSI)  weight-K-nearest neighbor (WKNN)
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