Master advanced Kalman filtering techniques with focus on derivation, implementation, and target tracking applications.
Master advanced Kalman filtering techniques with focus on derivation, implementation, and target tracking applications.
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
Derive linear Kalman filter equations
Implement robust estimation algorithms
Develop prediction and smoothing methods
Design target tracking systems
Handle non-standard conditions
Optimize filter performance
Skills you'll gain
This course includes:
7.8 Hours PreRecorded video
26 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course provides a deep understanding of linear Kalman filter theory and implementation. Students learn to derive filter equations, handle non-standard conditions, and implement robust solutions. The curriculum covers advanced topics including prediction, smoothing, and multiple-model approaches, with practical applications in target tracking. Special attention is given to numerical robustness and handling of real-world challenges.
Deriving the linear Kalman filter
Module 1 · 5 Hours to complete
Making the linear Kalman filter bulletproof
Module 2 · 5 Hours to complete
Extensions and refinements to linear Kalman filters
Module 3 · 5 Hours to complete
Target-tracking application using a linear 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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