SPOTTER: A novel asynchronous and independent WiFi and BLE fusion method based on particle filter for indoor positioning

Although global positioning system (GPS) has largely solved the problem of outdoor navigation, indoor positioning systems have remained a challenging problem over the last two decades. Sensor fusion has emerged as a state-of-the-art approach, utilizing various mobile phone sensors, particularly WiFi and Bluetooth low energy (BLE). An integrated method is crucial for achieving the best accuracy and the lowest energy consumption. This paper categorizes the different WiFi and BLE incorporation architectures based on signal scanning location and comprehensively investigates their influence. We propose a novel asynchronous and independent WiFi and BLE fusion method based on a particle filter (SPOTTER), which includes a new architecture and fusion algorithm. Unlike previous works, SPOTTER conducts WiFi signal scanning on WiFi access points. The fusion algorithm integrates the outcomes of independent WiFi and BLE subsystems as sensor data employing enhanced particle filtering to reduce localization error and latency. Experimental results demonstrate that SPOTTER outperforms fingerprint based fusion approaches by 27% in terms of positioning rate and significantly reduces WiFi and BLE interferences. Extensive experiments, including using different architectures and BLE beacon numbers, confirm SPOTTER's 35% improvement in accuracy and precision compared to fingerprint-based fusion.