计算机与现代化Issue(2)：44-51,8.DOI:10.3969/j.issn.1006-2475.2025.02.006
基于LSTM场景分类的行人自适应低功耗定位方案
Adaptive Low-power Localization Scheme for Pedestrians Based on LSTM Scene Classification
摘要
Abstract
To address the challenges of pedestrian localization accuracy and high power consumption in outdoor complex environ-ments,this paper proposes a low-power localization scheme based on scene classification for foot-mounted pedestrian navigation systems using GNSS/INS technology.This scheme collects GNSS,temperature and humidity sensor data,uses LSTM to classify typical outdoor scenes and adjusts the clock frequency of the MCU according to different scenes.Additionally,the scheme pro-poses an improved Sage-Husa method to mitigate the impact of GNSS outliers on localization results.The experimental results demonstrate that this solution achieves a scene classification accuracy of 97.64%with a system power consumption of only 193.074 mW.Compared with traditional ZUPT,GNSS,GNSS/INS integration and Sage-Husa methods,the proposed scheme re-duces the root mean square localization error by 83.15%,42.88%,21.91%and 11.49%respectively.Therefore,this scheme can improve pedestrian localization accuracy in outdoor environments with low system power consumption.关键词
行人定位/低功耗/LSTM/场景分类/Sage-Husa算法Key words
pedestrian navigation system/low power/LSTM/scene classification/Sage-Husa algorithm分类
计算机与自动化引用本文复制引用
余晴,江金光,谢东朋,刘江华..基于LSTM场景分类的行人自适应低功耗定位方案[J].计算机与现代化,2025,(2):44-51,8.基金项目
湖北科技学院博士科研启动基金资助项目(BK201801) （BK201801）
