RSSI and Device Pose Fusion for Fingerprinting-Based Indoor Smartphone Localization Systems

Complementing RSSI measurements at anchors with onboard smartphone accelerometer measurements is a popular research direction to improve the accuracy of indoor localization systems. This can be performed at different levels; for example, many studies have used pedestrian dead reckoning (PDR) and a filtering method at the algorithm level for sensor fusion. In this study, a novel conceptual framework was developed and applied at the data level that first utilizes accelerometer measurements to classify the smartphone's device pose and then combines this with RSSI measurements. The framework was explored using neural networks with room-scale experimental data obtained from a Bluetooth low-energy (BLE) setup. Consistent accuracy improvement was obtained for the output localization classes (zones), with an average overall accuracy improvement of 10.7 percentage points for the RSSI-and-device-pose framework over that of RSSI-only localization.

Automated summary

Complementing RSSI measurements with smartphone accelerometer measurements is a popular research direction in improving indoor localization systems. This study developed a novel conceptual framework that utilizes accelerometer measurements to classify smartphone's device pose and combines it with RSSI measurements. The framework, explored with neural networks and BLE experimental data, consistently improved the accuracy of the output localization classes.