Kalman, H-Infinity, and Nonlinear Estimation Approaches Training
Kalman, H-Infinity, and Nonlinear Estimation Approaches Training Course Description
This three-day Kalman, H-Infinity, and Nonlinear Estimation Approaches Training will introduce Kalman filtering and other state estimation algorithms in a practical way so that the student can design and apply state estimation algorithms for real problems. The course will also present enough theoretical background to justify the techniques and provide a foundation for advanced research and implementation. After taking this course the student will be able to design Kalman filters, H-infinity filters, and particle filters for both linear and nonlinear systems. The student will be able to evaluate the tradeoffs between different types of estimators. The algorithms will be demonstrated with freely available MATLAB programs.
• How can I create a system model in a form that is amenable to state estimation?
• What are some different ways to simulate a system?
• How can I design a Kalman filter?
• What if the Kalman filter assumptions are not satisfied?
• How can I design a Kalman filter for a nonlinear system?
• How can I design a filter that is robust to model uncertainty?
• What are some other types of estimators that may do better than a Kalman filter?
• What are the latest research directions in state estimation theory and practice?
• What are the tradeoffs between Kalman, H-infinity, and particle filters?
With onsite Training, courses can be scheduled on a date that is convenient for you, and because they can be scheduled at your location, you don’t incur travel costs and students won’t be away from home. Onsite classes can also be tailored to meet your needs. You might shorten a 5-day class into a 3-day class, or combine portions of several related courses into a single course, or have the instructor vary the emphasis of topics depending on your staff’s and site’s requirements.
Dynamic Systems Review. Linear systems. Nonlinear systems. Discretization. System simulation.
Random Processes Review. Probability. Random variables. Stochastic processes. White noise and colored noise.
Least Squares Estimation. Weighted least squares. Recursive least squares.
Time Propagation of States and Covariances.
The Discrete Time Kalman Filter. Derivation. Kalman filter properties.
Alternate Kalman filter forms. Sequential filtering. Information filtering. Square root filtering.
Kalman Filter Generalizations. Correlated noise. Colored noise. Steady-state filtering. Stability. Alpha-beta-gamma filtering. Fading memory filtering. Constrained filtering.
Optimal Smoothing. Fixed point smoothing. Fixed lag smoothing. Fixed interval smoothing.
Advanced Topics in Kalman Filtering. Verification of performance. Multiple-model estimation. Reduced-order estimation. Robust Kalman filtering. Synchronization errors.
H-infinity Filtering. Derivation. Examples. Tradeoffs with Kalman filtering.
Nonlinear Kalman Filtering. The linearized Kalman filter. The extended Kalman filter. Higher order approaches. Parameter estimation.
The Unscented Kalman Filter. Advantages. Derivation. Examples.
The Particle Filter. Derivation. Implementation issues. Examples. Tradeoffs.
Applications. Fault diagnosis for aerospace systems. Vehicle navigation. Fuzzy logic and neural network training. Motor control. Implementations in embedded systems.
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