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The prevalent deployment of Wi-Fi infrastructure provides a potentially lowcost way to track Wi-Fi enabled devices in a building. Many indoor Location Based Systems (LBS) aim to get sub-meter precision for grid position estimation, however such accuracy is not necessary for some indoor locationaware applications, such as conference room identification and elder-care alert etc. Our system is designed to track the mobile user at room level granularity with high accuracy and reliability.

In this paper, a probabilistic fingerprint approach is proposed based on quantized Received Signal Strength (RSS) measurements. In the training phase, a histogram based radio map is constructed for each room by storing various levels of RSS. The motion dynamics of the user is modelled as a Markov process and a Hidden Markov model (HMM) is applied to track the mobile user, where the hidden states comprise the possible room …