Particle Filters (and Navigation)
Offered By: University of Colorado System via Coursera
Course Description
Overview
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.
Syllabus
- A brute-force solution for highly nonlinear systems
- 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.
- How to approximate multidimensional integrals efficiently
- This week, you will learn the tricks we will use to approximate the brute-force solution.
- Developing and refining the particle-filter algorithm
- This week, you will put all of the tricks from week two together to implement (and then refine) the particle-filter method.
- Navigation application using a particle filter
- This week, you will learn how to apply the particle filter to an indoor navigation problem.
Taught by
Gregory Plett
Tags
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