2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN), Beijing, Çin, 5 - 08 Eylül 2022, ss.1-8
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.