ECEA 5850 Kalman-Filter Boot Camp and State-Estimation Application

1st course in the Applied Kalman Filtering.

Instructor: Greg Plett,ÌýPhD, Professor

This course introduces the Kalman filter as a method that can solve problems related to estimating the hidden internal state of a dynamic system. It develops the background theoretical topics in state-space models and stochastic systems. Learners will be presented with the steps of the linear Kalman filter and shown how to implement these steps in Octave code and how to evaluate the filter’s output.

Learning Outcomes

  • Implement conditions that cause a linear Kalman filter to produce poor results and analyze those results
  • Evaluate an open-loop state estimator in comparison with a Kalman-filter state estimator
  • Implement a linear Kalman filter state estimator in Octave and analyze its output
  • Implement a discrete-time simulation in Octave that generates data to be used with a Kalman filter
  • List the steps of the linear Kalman filter and the variables required to execute each step

Syllabus

Duration: 4Ìýhours

This week, you will learn what a Kalman filter is and generally what it does. You will be introduced to the roadmap for the course and the specialization, and will learn some applications that use Kalman filters.

Duration: 7Ìýhours

Kalman filters estimate the "state" of a system that is described using a "state-space model." This week, you will learn the background concepts in state-space models that are required in order to implement a Kalman filter.

Duration: 6Ìýhours

Systems whose state we would like to estimate are affected by unknown inputs ("disturbances" or "process noises") and their measurements are affected by sensor noises. These noises are modeled by random variables. This week, you will learn the background concepts in random variables that are required in order to implement a Kalman filter.

Duration: 5Ìýhours

Even though we have not yet derived the steps of the Kalman filter, it is instructive to gain insight into a Kalman filter's operation by watching it run. This week, you will learn how to implement a Kalman filter in Octave and see cases where it works well and where it fails (next course, you will learn why!).

Duration: 2Ìýhours

This module contains materials for the proctored final exam for MS-EE degree students.

To learn about ProctorU's exam proctoring, system test links, and privacy policy, visitÌýwww.colorado.edu/ecee/online-masters/current-students/proctoru.

Grading

Assignment
Percentage of Grade
Graded Assignment: Graded assignment for week 112.5%
Graded Assignment: Graded assignment for week 212.5%
Graded Assignment: Graded assignment for week 312.5%
Graded Assignment: Graded assignment for week 412.5%
Graded Assignment: ECEA 5850 Kalman Filter Boot Camp final exam50%

Letter Grade Rubric

Letter GradeÌý
Minimum Percentage
A93.3%
A-90%
B+86.6%
B83.3%
B-80%
C+76.6%
C73.3%
C-70%
D+66.6%
D60%
F0