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Nonlinear Kalman Filters (and Parameter Estimation)

Master advanced nonlinear Kalman filtering techniques including EKF, UKF, and parameter estimation methods.

Master advanced nonlinear Kalman filtering techniques including EKF, UKF, and parameter estimation methods.

This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Applied Kalman Filtering Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.

Instructors:

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Nonlinear Kalman Filters (and Parameter Estimation)

This course includes

21 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free course

What you'll learn

  • Implement extended Kalman filter algorithms

  • Develop sigma-point Kalman filters

  • Design adaptive estimation methods

  • Create parameter estimation systems

  • Apply joint state-parameter estimation

  • Optimize filter performance

Skills you'll gain

Extended Kalman Filter
Sigma-Point Kalman Filter
Parameter Estimation
Nonlinear Systems
State Estimation
Adaptive Filtering
System Modeling
Octave Programming
Numerical Methods
Control Systems

This course includes:

5.6 Hours PreRecorded video

27 assignments

Access on Mobile, Tablet, Desktop

FullTime access

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There are 4 modules in this course

This advanced course covers the theory and implementation of nonlinear Kalman filtering techniques. Students learn to derive and implement extended Kalman filter (EKF) and sigma-point Kalman filter (SPKF) algorithms for nonlinear systems. The curriculum includes adaptive methods, parameter estimation, and joint state/parameter estimation, with practical implementation using Octave code.

The extended Kalman filter

Module 1 · 5 Hours to complete

The sigma-point (unscented) Kalman filter

Module 2 · 4 Hours to complete

Extensions and refinements to nonlinear Kalman filters

Module 3 · 5 Hours to complete

Parameter estimation and joint estimation

Module 4 · 5 Hours to complete

Fee Structure

Instructor

Gregory Plett
Gregory Plett

5 rating

22 Reviews

72,282 Students

9 Courses

Leading Expert in Battery Systems and Control Engineering

Gregory Plett serves as Professor of Electrical and Computer Engineering at the University of Colorado Colorado Springs, where he has established himself as a pioneering researcher in battery management systems since 1998. His academic credentials include a B.Eng. from Carleton University and M.S.E.E. and Ph.D. degrees from Stanford University. His research focuses on advanced control systems for high-capacity batteries used in hybrid and electric vehicles, encompassing physics-based modeling, system identification, and state estimation. He has authored three influential volumes on Battery Management Systems, covering battery modeling, equivalent-circuit methods, and physics-based methods. As Director of the UCCS High-Capacity Battery Research and Test Laboratory, he leads cutting-edge research in battery pack simulation and management systems. His teaching portfolio includes advanced courses in control systems, battery dynamics, and management algorithms. A senior member of IEEE and life member of the Electrochemical Society, his work has significantly influenced the field of electric vehicle battery technology. His research innovations include developing methods for state-of-charge estimation, degradation modeling, and fast-charging protocols for battery packs.

Nonlinear Kalman Filters (and Parameter Estimation)

This course includes

21 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free course

Testimonials

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