Abstract -An algorithm for pedestrian navigation in indoor and urban canyon environments is presented. It considers platforms with low processing power and lowcost 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.
Seitz et al. REF present an algorithm for pedestrian navigation in indoor and urban canyon environments by using a combination of Wi-Fi positioning and DR, based on a Hidden Markov Model.
A Hidden Markov Model for urban navigation based on fingerprinting and pedestrian dead reckoning
2010 13th International Conference on Information Fusion
2010 13th International Conference on Information Fusion
Computer Science