- random processes: random constant, random walk, white noise, Gauss-Markov process
- Kalman filtering and smoothing
- inertial sensors (accelerometers and gyroscopes), principles and error sources
- mechanisation and navigation equations for inertial navigation
- INS initialisation and alignment
- INS error dynamics
- Integration of INS with GNSS and other sensors
After completing this course, students should be able to
- apply Kalman filtering and smoothing for solving navigation and surveying problems using GNSS and inertial sensors
- asses error sources for inertial navigation
- compute navigation solution (position, velocity and orientation) based on data from inertial sensors and GNSS positions
- identify limitations of INS
- analyze and evaluate the precision of position, velocity and orientation determined by integrated navigation systems (INS + GNSS + other sensors)
FAG5129 Theory of Errors
FAG5130 Satellite Based Positioning
Felteori FAG5129
GNSS-kurs FAG5123
Jekeli, Ch. (2001); Inertial Navigation Systems with Geodetic Applications. Walter de Gruyter, Berlin, New York.
R. G. Brown and P. Y. C. Hwang, “Introduction to Random Signals and Applied Kalman Filtering”, Wiley, 1992
If the course is discontinued, students may request to be examined during the following two academic years.
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
Written report
