"Indoor Pedestrian Tracking with Sparse RSS Fingerprints" by Qiuxia Chen, Dongdong Ding et al.

Article Title

Indoor Pedestrian Tracking with Sparse RSS Fingerprints

Authors

Qiuxia Chen, the School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen 518055, China.
Dongdong Ding, the CSE Department, Shanghai Jiao Tong University, Shanghai 200240, China.
Yue Zheng, the Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.

Keywords

localization, pedestrian tracking, sparse, RSS fingerprints

Abstract

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.