Orientation-Matched Multiple Modeling for RSSI-based Indoor Localization via BLE Sensors

Abstract

Internet of Things (IoT) has penetrated different aspects of our modern life where smart sensors enabled with Bluetooth Low Energy (BLE) are deployed increasingly within our surrounding indoor environments. BLE-based localization is, typically, performed based on Received Signal Strength Indicator (RSSI), which suffers from different drawbacks due to its significant fluctuations. In this paper, we focus on a multiplemodel estimation framework for analyzing and addressing effects of orientation of a BLE-enabled device on indoor localization accuracy. The fusion unit of the proposed method would merge orientation estimated by RSSI values and heading estimated by Inertial Measurement Unit (IMU) sensors to gain higher accuracy in orientation classification. In contrary to existing RSSIbased solutions that use a single path-loss model, the proposed framework consists of eight orientation-matched path loss models coupled with a multi-sensor and data-driven classification model that estimates the orientation of a hand-held device with high accuracy of 99%. By estimating the orientation, we could mitigate the effect of orientation on the RSSI values and consequently improve RSSI-based distance estimates. In particular, the proposed data-driven and multiple-model framework is constructed based on over 10 million RSSI values and IMU sensor data collected via an implemented LBS platform.