We present effective observation and motion models for tracking the position of a WiFi-equipped smartphone user in large continuous indoor environments. Our observation model can generate likelihoods at locations for which no calibration data is available. Three component motion models provide better proposal distribution of the user motion. These models being incorporated into the particle filter framework, our WiFi fingerprint-based localization algorithm can track the position of a smartphone user accurately in large indoor environments. Experiments carried with an Android smartphone in a multistory building illustrate the advantages of our models.
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