Master the fundamentals of Kalman filtering for state estimation in dynamic systems. Perfect for engineers and researchers.
Master the fundamentals of Kalman filtering for state estimation in dynamic systems. Perfect for engineers and researchers.
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:
English
Not specified
What you'll learn
Understand and implement Kalman filter algorithms
Develop state-space models for dynamic systems
Apply statistical concepts to state estimation
Implement filters using Octave/MATLAB
Evaluate filter performance and limitations
Troubleshoot common implementation issues
Skills you'll gain
This course includes:
8.4 Hours PreRecorded video
28 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course introduces students to Kalman filtering as a method for estimating hidden internal states of dynamic systems. The curriculum covers essential theoretical foundations including state-space models and stochastic systems, practical implementation in Octave/MATLAB, and performance evaluation. Students learn through a combination of theoretical concepts and hands-on programming exercises, preparing them to apply Kalman filtering techniques to real-world engineering problems.
What is the purpose of a Kalman filter?
Module 1 · 4 Hours to complete
What do I need to know about state-space models?
Module 2 · 6 Hours to complete
What do I need to know about random variables?
Module 3 · 6 Hours to complete
State-estimation application of a Kalman filter
Module 4 · 4 Hours to complete
Fee Structure
Instructor
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.
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