Advanced Kalman Theory
What: Advanced Kalman Filter Theory and Practice
February 17-21, 2003
Where: Best Western Heritage
Inn, Benicia, CA (San Francisco Bay Area)
Whom: Michael L. Carroll, Senior Software
Engineer, BEI Systron Donner Inertial Division
- Foundational Math: set theory; basic algebraic structures (groups, rings, fields);
metric topology and norms; measure
and integration theory; elementary Hilbert space theory; probability;
random processes (including white noise, random walk, random constants,
ramps, as well as practical spectral and correlation techniques).
- Dynamic Systems (deterministic): differential equations; distributions;
transforms (Laplace and Fourier); state space perspective.
- Dynamic Systems (stochastic) and Kalman filtering: stochastic
differential equations; errors and uncertainty; measurement modeling;
continuous filter derivation; system identification.
- Kalman Filtering: discrete formulation; nonlinear techniques; extended
Kalman filter; state vector augmentation; Markov models; real-time maximum
- Applications to navigation systems: terrestrial navigation concepts; inertial sensor (gyros and
accelerometers) error dynamics;inertial cluster error sources; quaternions;
attitude estimation; tightly and loosely coupled INS/GPS integration;
inertial calibration and alignment.
Sample Course Content: Measure Theory
Note: this sample shows only some of the more advanced theoretical topics
that will be briefly presented; understanding of these topics is not
crucial to successfully completing this course!
Sample Content from Previous Courses.
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and / or
MS Equation Editor.)
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Kalman filtering measure theory Carroll advanced course
covariance estimation optimal control
inertial navigation GPS