STUPEFY: Set-Valued Box Particle Filtering for Bluetooth Low Energy-Based Indoor Localization

With the rapid emergence of Internet of Things (IoT), we are more and more surrounded by smart connected devices with integrated sensing, processing, and communication capabilities. Bluetooth Low Energy (BLE), referred to as Bluetooth Smart, is considered as the main-stream technology to perform identification and localization/tracking in IoT applications. While single-model BLE-based tracking has been investigated from different aspects, application of multi-model (hybrid) solutions are still in their infancy. In this regard, the letter proposes a novel BLE-based tracking framework, referred to as the STUPEFY, which incorporates set-valued information within box particle filtering context. More specifically, the proposed multiple-model STUPEFY framework consists of three integrated modules, i.e., an intriguing Smoothing Module based on Kalman filtering to reduce the Received Signal Strength Indicator (RSSI) fluctuations and facilitate comparison of Gaussian models of the RSSI values in distribution with the learned ones; A learning-based model (Coordination Module) utilized in an intuitive fashion to provide/construct a coarse estimate of the target's location together with the smallest axes-aligned box containing the ellipsoid associated with each zone's learned RSSI distribution, and; A novel set-valued box particle filtering (SBPF) approach (Micro-Localization Module). The proposed STUPEFY framework is evaluated based on real BLE datasets and results illustrate significant potentials in terms of improving overall BLE-based achievable tracking accuracy.