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A HIDDEN MARKOV MODEL FOR INDOOR USER TRACKING BASED ON WIFI FINGERPRINTING AND STEP DETECTION
| Content Provider | CiteSeerX |
|---|---|
| Author | Vu, D. H. Tran Drueke, C. Schmalenstroeer, J. Haeb-Umbach, R. Hoang, M. K. |
| Abstract | In this paper we present a modified hidden Markov model (HMM) for the fusion of received signal strength index (RSSI) information of WiFi access points and relative po-sition information which is obtained from the inertial sensors of a smartphone for indoor positioning. Since the states of the HMM represent the potential user locations, their number determines the quantization error introduced by discretizing the allowable user positions through the use of the HMM. To reduce this quantization error we introduce ”pseudo ” states, whose emission probability, which models the RSSI mea-surements at this location, is synthesized from those of the neighboring states of which a Gaussian emission probability has been estimated during the training phase. The experimen-tal results demonstrate the effectiveness of this approach. By introducing on average two pseudo states per original HMM state the positioning error could be significantly reduced without increasing the training effort. Index Terms — Indoor positioning, fingerprint, pseudo node, step detection, RSSI measurement |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Emission Probability Step Detection Allowable User Position Positioning Error Quantization Error Pseudo State Pseudo Node Index Term Indoor Positioning Original Hmm State Training Phase Modified Hidden Markov Model Indoor Positioning Rssi Measurement Inertial Sensor Potential User Location Received Signal Strength Index Training Effort Wifi Access Point Relative Po-sition Information Neighboring State Gaussian Emission Probability Rssi Mea-surements |
| Content Type | Text |
| Resource Type | Article |
