Live RSSI Filtering for Indoor Positioning with Bluetooth Low-Energy

Abstract

This work investigates the performance improvement of an indoor positioning and tracking system through a real-time preprocessing of the measured received signal strength indicator (RSSI) data. The system itself is based on a hidden Markov model constructed upon a priorly estimated radio frequency map for the measurement density, and a Gaussian (diffusion) distribution for the transition density. The positions are estimated as the latent variables via particle filter algorithms that are fed with the filtered RSSI data as observations. We first compare the three nonlinear time window filtering techniques, mean, median and maximal filters on the streaming RSSI data captured by the distributed Bluetooth low energy (BLE) sensors. Seeing the performance boost of the maximal filter strategy with a standard particle filter implementation, we further investigate various model parameters in two particle filter applications: static and adaptive particle filters. The maximal filter preprocessing technique is shown to increase the positioning performance by more than 20% for real-time applications. The performance boost has still space to perform 40% better with better approximation preferences compared to raw RSSI readings.