Particle filter-based BLE and IMU fusion algorithm for indoor localization

Indoor positioning systems have become increasingly popular due to the growing demand for location-based services in various domains. While effective outdoors, traditional Global Positioning System (GPS) technologies are often unsuitable for indoor environments due to their reliance on satellite signals, which are severely attenuated or obstructed indoors. This limitation underscores the need for alternative indoor localization solutions. Bluetooth Low Energy (BLE) has emerged as a favorable solution for indoor localization, thanks to its widespread deployment in smartphones and other consumer devices, providing a low-cost and energy-efficient option. However, BLE-based localization is frequently challenged by signal interference and multipath effects, which degrade the system’s accuracy. This work presents a novel approach that fuses BLE and Inertial Measurement Unit (IMU) data to address these limitations and enhance indoor positioning accuracy. Our approach employs a Multi-Carrier Phase Difference method for precise BLE-based distance estimation, which is subsequently combined with IMU data through a particle filter framework. The IMU data are processed using the Madgwick filter and a step-detection model to capture motion dynamics accurately. The fusion model demonstrates substantial improvements in localization accuracy, with experimental results showing up to a 25% reduction in measurement errors, especially in challenging and complex indoor environments.