A Model-Based BLE Indoor Positioning System Using Particle Swarm Optimization

Indoor positioning systems (IPSs) have emerged as a research topic in mobile computing, enabling the tracking and location of mobile devices in indoor environments. In model-based IPSs, the received signal strength indicator (RSSI) is used to estimate the distance between wireless signal receivers and transmitters using signal propagation models. However, the indoor environment presents challenges that make distance estimation using RSSI difficult. In this article, we propose a new IPS that combines particle swarm optimization (PSO) with signal propagation models to improve the accuracy of mobile device positioning. The PSO algorithm is used to optimize the position estimation process by generating different particles in the map, while the signal propagation model is used to model the attenuation and reflection of wireless signals in each particle. Our MIPS-PSO system does not require any prior training nor any knowledge of the best parameters of the signal propagation model. We evaluated the performance of our system using data collected in a real indoor environment with Bluetooth-low-energy (BLE) devices. Our results show that the MIPS-PSO achieves an average error of 2.57 m, an improvement of 40% when compared to a traditional trilateration, model-based IPS