基于AAPC、CS与卡尔曼滤波的WiFi室内定位跟踪算法  

A WiFi Indoor Positioning and Tracking Algorithm Based on AAPC,CS and Kalman Filtering

作　　者：胡久松 孙英杰[1] 黄晓峰[1] 谷志茹[1] 李浩[1] HU Jiusong;SUN Yingjie;HUANG Xiaofeng;GU Zhiru;LI Hao(College of Railway Transportation,Hunan University of Technology,Zhuzhou Hunan 412007,China)

机构地区：[1]湖南工业大学轨道交通学院,湖南株洲412007

出　　处：《湖南工业大学学报》2024年第6期71-78,共8页Journal of Hunan University of Technology

基　　金：湖南省教育厅优秀青年基金资助项目(293832);湖南省自然科学基金资助项目(2023JJ50198,2022JJ50005)

摘　　要：针对基于位置指纹的WiFi室内定位技术的定位精度尚未达到实际应用要求的问题,提出一种融合自适应仿射传播(AAPC)、压缩感知(CS)与卡尔曼滤波的WiFi室内定位跟踪算法。其中,离线阶段使用AAPC算法生成具有最优聚类效应性能的聚类指纹,在线阶段采用CS与最近邻算法进行位置估计。最后,通过将卡尔曼滤波与物理限制相集成来进行定位跟踪。通过采集大量真实实验数据,证明了所开发的算法具有更高的定位精度和更准确的轨迹跟踪效果。In view of the flaw that the positioning accuracy of WiFi indoor positioning technology based on position fingerprint fails to meet the practical application requirements,a WiFi indoor positioning and tracking algorithm integrating adaptive affine propagation(AAPC),compressed sensing(CS),and Kalman filtering has thus been proposed.Among them,AAPC algorithm is used to generate clustering fingerprints with the best clustering effect performance in the offine stage,followed by a position estimation during the online phase with CS and nearest neighbor algorithms adopted.Finally,localization and tracking are performed by combining Kalman filtering with physical constraints.Based on a collection of a large amount of real experimental data,it has been proven that the developed algorithm is characterized with a higher positioning accuracy and more accurate trajectory tracking effect.

关 键 词：WiFi室内定位 自适应仿射传播 压缩感知 卡尔曼滤波 

分 类 号：TN92[电子电信—通信与信息系统]