基于PDR与信号指纹的室内融合定位方法  

Indoor Fusion Positioning Method Based on PDR and Signal Fingerprint

作　　者：孙顺远 周隽阳 秦宁宁 SUN Shunyuan;ZHOU Junyang;QIN Ningning(Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education,School of Internet of Things Engineering,Jiangnan University,Wuxi Jiangsu 214122,China)

机构地区：[1]江南大学物联网工程学院轻工过程先进控制教育部重点实验室,江苏无锡214122

出　　处：《传感技术学报》2024年第8期1362-1368,共7页Chinese Journal of Sensors and Actuators

基　　金：中国高校产学研创新基金“新一代信息技术创新项目”(2021ITA10003)。

摘　　要：室内复杂环境中,为了解决行人航位推算法(Pedestrian Dead Reckoning, PDR)存在误差累积、指纹定位法存在结果回跳的问题,提出PDR与信号指纹联合作用下的融合定位方法。基于峰值步态检测,引入模型动态调整峰谷阈值以匹配行走变速,提高检测过程对运动状态变化的适应性。以运动时空信息修正信号空间的相似点权值,加权求和克服环境干扰提升指纹定位精度。利用扩展卡尔曼滤波(Extended Kalman Filter, EKF)对融合系统模型线进行性化提取,通过对待定位点的迭代预测与更新的过程实现稳定且准确的寻优结果。实验验证所提方法较单一PDR或信号指纹可分别提升约47.7%和14.1%的定位精度,平均定位误差可减小到2.138 m。In a complex indoor environment,in order to solve the problems of cumulative error in pedestrian dead reckoning(PDR)and fluctuation in fingerprint positioning method,a fusion positioning method under the joint action of PDR and signal fingerprint is pro-posed.Based on peak gait detection,an adaptive model is introduced to dynamically adjust the peak and valley threshold to match the variable walking speed,which improves the adaptability of the detection process to changes in motion state.The weights of similar points in signal space are corrected with motion spatiotemporal information,and the weighted sum overcomes environmental interference to im-prove fingerprint positioning accuracy.Extended Kalman Filter(EKF)linearizes the fusion system model,and achieves stable and accu-rate optimization results through the iterative prediction and update process of the positioning points.Experiments verify that the method proposed can improve the positioning accuracy by about 47.7%and 14.1%compared with a single PDR or signal fingerprint,and the average positioning error can be reduced to 2.138 m.

关 键 词：室内定位 融合定位 扩展卡尔曼滤波 行人航位推算 指纹定位 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]