--- res: bibo_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 position 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 â??pseudoâ?? states, whose emission probability, which models the RSSI measurements 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 experimental 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.@eng bibo_authorlist: - foaf_Person: foaf_givenName: Manh Kha foaf_name: Hoang, Manh Kha foaf_surname: Hoang - foaf_Person: foaf_givenName: Joerg foaf_name: Schmalenstroeer, Joerg foaf_surname: Schmalenstroeer foaf_workInfoHomepage: http://www.librecat.org/personId=460 - foaf_Person: foaf_givenName: Christian foaf_name: Drueke, Christian foaf_surname: Drueke - foaf_Person: foaf_givenName: Dang Hai foaf_name: Tran Vu, Dang Hai foaf_surname: Tran Vu - foaf_Person: foaf_givenName: Reinhold foaf_name: Haeb-Umbach, Reinhold foaf_surname: Haeb-Umbach foaf_workInfoHomepage: http://www.librecat.org/personId=242 dct_date: 2013^xs_gYear dct_language: eng dct_title: A Hidden Markov Model for Indoor User Tracking Based on WiFi Fingerprinting and Step Detection@ ...