Non-Gaussian Ble-Based Indoor Localization Via Gaussian Sum Filtering Coupled With Wasserstein Distance

With recent breakthroughs in signal processing, communication and networking systems, we are more and more surrounded by smart connected devices empowered by the Internet of Thing (IoT). Bluetooth Low Energy (BLE) is considered as the main-stream technology to perform identification and localization/tracking in IoT applications. Indoor localization applications within smart cities, typically, start by observing messages transmitted by BLE beacons and then utilization of Received Signal Strength Indicator (RSSI) to provide location estimates. RSSI signals are, however, prone to significant fluctuations. The main challenge is that multipath fading and drastic fluctuations in the indoor environment result in complex non-Gaussian RSSI measurements, necessitating the need to smooth RSSIs for development of BLE-based localization applications. In contrary to existing solutions, where RSSIs are assumed to have normal statistical properties, in this paper, a Gaussian Sum Filter (GSF) approach is designed to more realistically model the non-Gaussian nature of RSSIs. To maintain acceptable computational load, the number of components in the GSF is collapsed into a single Gaussian term with a novel Wasserstein Distance (WD)-Based Gaussian Mixture Reduction (GMR) algorithm. The simulation results based on real collected RSSI signals confirm the success of the proposed WD-based GSF framework compared to its conventional counterparts.