Indoor Pedestrian Tracking with Sparse RSS Fingerprints

• 来源期刊: 清华大学学报自然科学版（英文版）
• 年，卷（期）: 2018,（1）
• 页码范围: 95-103
• 页数: 9页
• 语种: 英文
• DOI: 10.26599/TST.2018.9010026

摘要

Indoor pedestrian localization is of great importance for diverse mobile applications.Many indoor localization approaches have been proposed;among them,Radio Signal Strength (RSS)-based approaches have the advantage of existing infrastructures and avoid the cost of infrastructure deployment.However,the RSS-based localization approaches suffer from poor localization accuracy when the RSS fingerprints are sparse,as illustrated by actual experiments in this study.Here,we propose a novel indoor pedestrian tracking approach for smartphone users;this approach provides a high localization accuracy when the RSS fingerprints are sparse.Besides using the RSS fingerprints,this approach also utilizes the inertial sensor readings on smartphones.This approach has two components:(i) dead-reckoning subsystem that counts the number of walking steps with off-the-shelf inertial sensor readings on smartphones and (ii) particle filtering that computes the locations with only sparse RSS readings.The proposed approach is implemented on Android-based smartphones.Extensive experiments are carried out in both small and large testbeds.The evaluation results show that the tracking approach can achieve a high accuracy of 5 m (up to 95％) in indoor environments with only sparse RSS fingerprints.