Estimation of indoor position and motion direction for smartphones using DNN to BLE beacon signal strength

Abstract

We investigated a method to simultaneously detect the location and motion direction of a pedestrian walking indoors. Multiple time instances of Received Signal Strength Indicator (RSSI) readings from multiple Bluetooth Low Energy (BLE) beacons on a smartphone held by a pedestrian, traveling in one of 9 directions, were fed to a trained Deep Neural Network (DNN), and the location of this smartphone, as well as the direction of its motion, was simultaneously estimated. Previous experiments in a $4m\times 7m$ area showed estimated location accuracy of 0.91m, and average estimation accuracy of 83.5% in 5 directions. In this paper, we significantly increased the quality of the RSSI training data, as well as the number of motion direction to nine from five. The estimated location accuracy of 0.439m, and average estimated direction accuracy of 81.2% in 9 directions was shown.