Abstract
Evaluation results for a simple test scenario with two op- positely intersecting trajectories demonstrated a significant im- provement of location accuracy with the Directional HMM algorithm. Further results for a scenario with realistic simulation based movement trajectories also showed improvements for 60% of the cases, however only if the HMM models are trained with usually unknown true trajectories. When trained with inaccurate location estimations, the HMM based algorithms showed no benefit compared to just using the localization system.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the European Wireless, 2013. EW. 19th European Wireless Conference |
| Number of pages | 6 |
| Publisher | VDE Verlag GMBH |
| Publication date | 16 Apr 2013 |
| ISBN (Electronic) | 978-3-8007-3498-6 |
| Publication status | Published - 16 Apr 2013 |
| Event | The 19th European Wireless Conference - Guildford, United Kingdom Duration: 16 Apr 2013 → 18 Apr 2013 |
Conference
| Conference | The 19th European Wireless Conference |
|---|---|
| Country | United Kingdom |
| City | Guildford |
| Period | 16/04/2013 → 18/04/2013 |
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Bibliographical note
SNO/CNO for "European Wireless Conference" series: 5010943Cite this
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Directional Hidden Markov Model for Indoor Tracking of Mobile Users and Realistic Case Study. / Nielsen, Jimmy Jessen; Amiot, Nicolas; Madsen, Tatiana Kozlova.
Proceedings of the European Wireless, 2013. EW. 19th European Wireless Conference. VDE Verlag GMBH, 2013.Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review
TY - GEN
T1 - Directional Hidden Markov Model for Indoor Tracking of Mobile Users and Realistic Case Study
AU - Nielsen, Jimmy Jessen
AU - Amiot, Nicolas
AU - Madsen, Tatiana Kozlova
N1 - SNO/CNO for "European Wireless Conference" series: 5010943
PY - 2013/4/16
Y1 - 2013/4/16
N2 - Indoors, mobile users tend to exhibit some level of determinism in their movement patterns during a day, for example when arriving to their office, going for coffee, going for lunch break, picking up print outs, etc. In this work we exploit this determinism to improve the accuracy of indoor localization systems. We consider two Hidden Markov Model (HMM) based filtering algorithms that use previous observations to estimate a user’s most likely movement trajectory, given a sequence of inaccurate location coordinates. The proposed Directional HMM algorithm is able to learn user habits by discriminating between different movement directions when populating the state transition probability matrix from training data. The proposed algorithm is compared to a Standard HMM algorithm that does not distinguish different movement directions.Evaluation results for a simple test scenario with two op- positely intersecting trajectories demonstrated a significant im- provement of location accuracy with the Directional HMM algorithm. Further results for a scenario with realistic simulation based movement trajectories also showed improvements for 60% of the cases, however only if the HMM models are trained with usually unknown true trajectories. When trained with inaccurate location estimations, the HMM based algorithms showed no benefit compared to just using the localization system.
AB - Indoors, mobile users tend to exhibit some level of determinism in their movement patterns during a day, for example when arriving to their office, going for coffee, going for lunch break, picking up print outs, etc. In this work we exploit this determinism to improve the accuracy of indoor localization systems. We consider two Hidden Markov Model (HMM) based filtering algorithms that use previous observations to estimate a user’s most likely movement trajectory, given a sequence of inaccurate location coordinates. The proposed Directional HMM algorithm is able to learn user habits by discriminating between different movement directions when populating the state transition probability matrix from training data. The proposed algorithm is compared to a Standard HMM algorithm that does not distinguish different movement directions.Evaluation results for a simple test scenario with two op- positely intersecting trajectories demonstrated a significant im- provement of location accuracy with the Directional HMM algorithm. Further results for a scenario with realistic simulation based movement trajectories also showed improvements for 60% of the cases, however only if the HMM models are trained with usually unknown true trajectories. When trained with inaccurate location estimations, the HMM based algorithms showed no benefit compared to just using the localization system.
M3 - Article in proceeding
BT - Proceedings of the European Wireless, 2013. EW. 19th European Wireless Conference
PB - VDE Verlag GMBH
ER -