A Hidden Markov Model for pedestrian navigation

Abstract

We present an algorithm for pedestrian navigation optimized for smart mobile platforms using the present low-cost sensors and the limited processing power. The algorithm is based on a Hidden Markov Model that combines Wi-Fi positioning and dead reckoning. The hidden states are the positions of the Wi-Fi fingerprints in the database. The state transition includes dead reckoning based on step length estimation from acceleration measurements and compass heading calculated from magnetic field measurements. In the measurement update a database correlation of the actual Wi-Fi signal strength measurements with the stored values in the fingerprints has been performed. In simulations and tests we demonstrate that in this way ambiguities common in Wi-Fi positioning can be reduced. Therefor, higher accuracy and robustness can be achieved.