ECEA 5853 Particle Filters and Navigation Application
4th course in the Applied Kalman Filtering.
Instructor: Greg Plett,ÌýPhD, Professor
As the final course in the Applied Kalman Filtering specialization, you will learn how to develop the particle filter for solving strongly nonlinear state-estimation problems. You will learn about the Monte-Carlo integration and the importance density. You will see how to derive the sequential importance sampling method to estimate the posterior probability density function of a system’s state. You will encounter the degeneracy problem for this method and learn how to solve it via resampling. You will learn how to implement a robust particle-filter in Octave code and will apply it to an indoor-navigation problem.
Prior knowledge needed:Ìý
- ECEA 5850 Kalman-Filter Boot Camp and State-Estimation Application
- ECEA 5851 Kalman Filter Deep Dive and Target-Tracking Application
- ECEA 5852 Nonlinear Kalman Filters, Parameter-Estimation Application
Learning Outcomes
- Execute a particle filter implemented in Octave to solve an indoor navigation problem and analyze its outputs.
- Understand the components of the RSSI model and what they mean.
- Analyze design choices when implementing a particle filter for the navigation problem.
- Understand the basis of operation of triangulation and trilateration.
- Understand the principal concepts of the navigation problem.
Syllabus
Duration: 5Ìýhours
This week, you will learn a computationally intensive method to estimate the state of highly nonlinear systems, where the pdfs do not need to be Gaussian.
Duration: 5.5Ìýhours
This week, you will learn the tricks we will use to approximate the brute-force solution.
Duration: 6.5Ìýhours
This week, you will put all of the tricks from week two together to implement (and then refine) the particle-filter method.
Duration: 4 hours
This week, you will learn how to apply the particle filter to an indoor navigation problem.
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 5853 Particle 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 |