A Hidden Markov Model for urban navigation based on fingerprinting and pedestrian dead reckoning

Abstract

An algorithm for pedestrian navigation in indoor and urban canyon environments is presented. It considers platforms with low processing power and low-cost sensors. A combination of Wi-Fi positioning and dead reckoning, based on a Hidden Markov Model, is used. The positions of the Wi-Fi fingerprints in the database are used as hidden states. Dead reckoning is taken for state transition and a database correlation of the Wi-Fi signal strength measurements is performed in the measurement update. The dead reckoning consists of an accelerometer driven step length estimation and a magnetic field based heading calculation. Simulations and tests demonstrate that in this way ambiguities common in Wi-Fi positioning can be solved and outages can be bridged. Therefore, higher accuracy and robustness can be achieved.