ECEA 5852 Nonlinear Kalman Filters, Parameter-Estimation Application
3rd course in the Applied Kalman Filtering.
Instructor: Greg Plett,ÌýPhD, Professor
As a follow-on course to "Linear Kalman Filter Deep Dive", this course derives the steps of the extended Kalman filter and the sigma-point Kalman filter for estimating the state of nonlinear dynamic systems. You will learn how to implement these filters in Octave code and compare their results. You will be introduced to adaptive methods to tune Kalman-filter noise-uncertainty covariances online. You will learn how to estimate the parameters of a state-space model using nonlinear Kalman filters.
Prior knowledge needed:Ìý
- ECEA 5850 Kalman-Filter Boot Camp and State-Estimation Application
- ECEA 5851 Kalman Filter Deep Dive and Target-Tracking Application
Learning Outcomes
- Execute the joint EKF and joint SPKF code provided to you and compare their outputs.
- Compare and contrast dual and joint Kalman filtering for simultaneous state and parameter estimation.
- Execute the EKF code provided for implementing parameter estimation and evaluate its outputs.
- Execute the SPKF code provided for implementing parameter estimation and evaluate its outputs.
- Understand the similarities and differences between state and parameter estimation using a nonlinear Kalman filter
Syllabus
Duration: 5Ìýhours
This week, you will learn how to implement the extended Kalman filter to estimate the state of a nonlinear system.
Duration: 4Ìýhours
This week, you will learn how to implement the sigma-point Kalman filter to estimate the state of a nonlinear system.
Duration: 5.5Ìýhours
In this module, you will learn the basics of Threading and Multi Thread Synchronization in Linux system programming using POSIX. ÌýYou will also learn about the Buildroot build system and build a QEMU Image using Buildroot.
Duration: 6Ìýhours
In this module, you will learn the fundamentals of signal handling and time management in Linux System Programming. ÌýYou will learn the basic components of Embedded Linux debugging. ÌýYou will implement a socket server application and deploy on a QEMU based Embedded System using Buildroot.
Duration: 2Ìýhours
This module contains materials for the proctored final exam for MS-EE degree students. If you've upgraded to the for-credit version of this course, please make sure you review the additional for-credit materials in the Introductory module and anywhere else they may be found.
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 1 | 12.5% |
| Graded Assignment: Graded assignment for week 2 | 12.5% |
| Graded Assignment: Graded assignment for week 3 | 12.5% |
| Graded Assignment: Graded assignment for week 4 | 12.5% |
| Graded Assignment: ECEA 5852 Nonlinear Kalman Filters final exam | 50% |
Letter Grade Rubric
Letter GradeÌý | Minimum Percentage |
| A | 93.3% |
| A- | 90% |
| B+ | 86.6% |
| B | 83.3% |
| B- | 80% |
| C+ | 76.6% |
| C | 73.3% |
| C- | 70% |
| D+ | 66.6% |
| D | 60% |
| F | 0 |