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Hidden Markov model-based 3D path-matching using raytracing-generated Wi-Fi models

We propose an efficient approach to probabilistic 3D indoor path-matching and localization based on Wi-Fi-signal measurements using Hidden Markov Model-based (HMM) algorithms. Given a 3D model of the building, we derive high-resolution emission probabilities and transition probabilities from raytracing-generated Wi-Fi signal propagations. Therefore we use both the generated signal-strength values and the geometric information of the 3D model. Based on the emission and transition probabilities and a sequence of Wi-Fi signal measurements provided by the client, the HMM-based algorithm computes the most probable path through the building.