Indoor Pedestrian Tracking with Sparse RSS Fingerprints

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 of5 m(up to 95%) in indoor environments with only sparse RSS fingerprints.