Robust Heading Estimation Indoors
2013 (English)Report (Other academic)
Indoor positioning in unknown environments is crucial for rescue personnel and future infotainment systems. Dead-reckoning inertial sensor data gives accurate estimate of distance, for instance using zero velocity updates, while the heading estimation problem is inherently more difficult due to the large degree of magnetic disturbances indoors. We propose a Kalman filter bank approach based on supporting a magnetic compass with gyroscope turn rate information, where a hidden Markov model is used to model the presence of magnetic disturbances. In parallel, we suggest to run a robust heading estimation system based on data from a sliding window. The robust estimate is used to detect filter divergence, and to restart the filter when needed. The underlying assumptions and the heading estimation performance are supported in field trials using more than 500 data sets from more than 50 venues in 5 continents.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013. , 13 p.
LiTH-ISY-R, ISSN 1400-3902 ; 3061
IdentifiersURN: urn:nbn:se:liu:diva-91393OAI: oai:DiVA.org:liu-91393DiVA: diva2:617588
FundereLLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsLinnaeus research environment CADICS