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University of Colorado System

Particle Filters (and Navigation)

University of Colorado System via Coursera

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Overview

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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

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