Particle Filters (and Navigation)
Master particle filter implementations for nonlinear state estimation, with applications in indoor navigation and Bayesian inference.
Master particle filter implementations for nonlinear state estimation, with applications in indoor navigation and Bayesian inference.
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.
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What you'll learn
Implement robust particle-filter algorithms in Octave
Apply Monte-Carlo integration methods effectively
Develop sequential importance sampling techniques
Solve indoor navigation problems using particle filters
Implement Bayesian inference state-estimation solutions
Skills you'll gain
This course includes:
6.23 Hours PreRecorded video
2 quizzes, 26 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This advanced course focuses on developing particle filters for solving strongly nonlinear state-estimation problems. Students learn Monte-Carlo integration, importance density concepts, and sequential importance sampling methods for estimating posterior probability density functions. The curriculum covers implementation of robust particle filters in Octave, with practical applications in indoor navigation. Through hands-on programming exercises and theoretical study, students master both the mathematical foundations and practical applications of particle filtering techniques.
A brute-force solution for highly nonlinear systems
Module 1 · 5 Hours to complete
How to approximate multidimensional integrals efficiently
Module 2 · 5 Hours to complete
Developing and refining the particle-filter algorithm
Module 3 · 6 Hours to complete
Navigation application using a particle filter
Module 4 · 6 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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