Course

CIVIL-606: Inference for large-scale time series with application to sensor fusion

Summary

Large-scale time series analysis is performed by a new statistical tool that is superior to other estimators of complex state-space models. The identified stochastic dependences can be used for sensor fusion by Bayesian (e.g. Kalman) filtering or for studying changes in natural/biological phenomena.

Lectures

Instructors (2)

Jan Skaloud

Expertise Satellite positioning, inertial and integrated navigation Sensor orientation and calibration, attitude determination Mobile mapping, airborne laser scanning Adjustment methods, Kalman Filtering Mission Teaching in 3 EPFL sections and 2 faculties, director of ESO lab. Books / Monographs J. Skaloud and M. Cramer, «Data Capture,» in GIS Handbook, W. Kresse and D. Danko, Eds. Springer, 2011. D. Lichti and J. Skaloud, «Registration and Calibration,» in Airborne and terrestrial laser scanning, H.-G. Ma

Stéphane Guerrier

Official source

https://edu.epfl.ch/coursebook/en/inference-for-large-scale-time-series-with-application-to-sensor-fusion-CIVIL-606

Moodle Page

http://go.epfl.ch/CIVIL-606

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Summary

Lectures

Instructors (2)

Jan Skaloud

Stéphane Guerrier

Official source

https://edu.epfl.ch/coursebook/en/inference-for-large-scale-time-series-with-application-to-sensor-fusion-CIVIL-606

Moodle Page

http://go.epfl.ch/CIVIL-606

About this result

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