What: Advanced Kalman Filter Theory and Practice

When: February 17-21, 2003

Where:  Best Western Heritage Inn, Benicia, CA (San Francisco Bay Area)

By 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 likelihood estimation.
• 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.

Course Details

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. (Note: it may be necessary for you to have MS Internet Explorer and / or MS Equation Editor.)

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