University of Colorado System
Applied Kalman Filtering Specialization
University of Colorado System

Applied Kalman Filtering Specialization

Learn how to Design and Implement Kalman Filters. Linear and nonlinear Kalman filters and particle filters for state estimation, target tracking, parameter estimation, and navigation

Gregory Plett

Instructor: Gregory Plett

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Intermediate level

Recommended experience

4 months
at 5 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

4 months
at 5 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • How to design and implement robust linear and nonlinear Kalman filters and particle filters to solve important engineering state-estimation problems.

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Taught in English
Recently updated!

September 2024

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Specialization - 4 course series

What you'll learn

Skills you'll gain

Category: Applying Kalman filters for state estimation
Category: Learning state-space models for Kalman filters
Category: Understanding the purpose of a Kalman filter
Category: Learning random variables and random processes for Kalman filters
Category: Learning the steps of a linear Kalman filter

What you'll learn

Skills you'll gain

Category: Describing the role of all Kalman-filter variables
Category: Understanding purpose of sequential-probabilistic-inference steps
Category: Modifying Kalman-filter steps to enable applications that violate the standard assumptions
Category: Modifying Kalman-filter steps to make it more robust
Category: Implement a Kalman filter for a target-tracking application

What you'll learn

Skills you'll gain

Category: Implement sigma-point Kalman filter (SPKF) and unscented Kalman filter (UKF)
Category: Implement extended Kalman filter (EKF)
Category: Implement parameter estimators using nonlinear Kalman filters
Category: Implement cubature Kalman filter (CKF)
Category: Implement adaptive extended Kalman filter (AEKF)

What you'll learn

Skills you'll gain

Category: Implement a robust particle-filter algorithm in Octave
Category: Implementing Bayesian inference state-estimation solution
Category: Apply a particle filter to solve an indoor-navigation problem
Category: Implement sequential importance sampling in Octave
Category: Implement Monte-Carlo integration methods

Instructor

Gregory Plett
University of Colorado System
9 Courses71,346 learners

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