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Particle Filters (and Navigation)

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Particle Filters (and Navigation)

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

Details to know

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Assessments

24 assignments

Taught in English

Build your subject-matter expertise

This course is part of the Applied Kalman Filtering Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

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.

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.

What's included

7 videos12 readings5 assignments1 discussion prompt1 ungraded lab

7 videos•Total 103 minutes
  • 4.1.1: Welcome to the course!•8 minutes
  • 4.1.2: Review of key concepts•17 minutes
  • 4.1.3: Developing the integral framework for general Bayesian recursion•20 minutes
  • 4.1.4: How to approximate integrals numerically•12 minutes
  • 4.1.5: Implementing Bayesian inference via numeric integration•18 minutes
  • 4.1.6: Octave code to implement Bayesian inference•26 minutes
  • 4.1.7: Summary of "A brute-force solution for highly nonlinear systems" module plus next steps•3 minutes
12 readings•Total 120 minutes
  • Frequently Asked Questions•10 minutes
  • Course Resources•10 minutes
  • How to Use Discussion Forums•10 minutes
  • Earn a Course Certificate•10 minutes
  • Are you interested in earning an online MSEE degree?•10 minutes
  • Notes for Lesson 4.1.1•10 minutes
  • Notes for Lesson 4.1.2•10 minutes
  • Notes for Lesson 4.1.3•10 minutes
  • Notes for Lesson 4.1.4•10 minutes
  • Notes for Lesson 4.1.5•10 minutes
  • Notes for Lesson 4.1.6•10 minutes
  • Notes for Lesson 4.1.7•10 minutes
5 assignments•Total 70 minutes
  • Graded assignment for week 1•30 minutes
  • Practice assignment for Lesson 4.1.3•10 minutes
  • Practice assignment for Lesson 4.1.4•10 minutes
  • Practice assignment for Lesson 4.1.5•10 minutes
  • Practice assignment for Lesson 4.1.6•10 minutes
1 discussion prompt•Total 10 minutes
  • Introduce yourself•10 minutes
1 ungraded lab•Total 30 minutes
  • Jupyter notebook implementing brute-force Bayesian inference•30 minutes

This week, you will learn the tricks we will use to approximate the brute-force solution.

What's included

6 videos6 readings6 assignments4 ungraded labs

6 videos•Total 71 minutes
  • 4.2.1: Introducing the Monte-Carlo method for approximating an integral•13 minutes
  • 4.2.2: The importance of an importance density•12 minutes
  • 4.2.3: Weight normalization•17 minutes
  • 4.2.4: The impulse function•15 minutes
  • 4.2.5: How to visualize a pdf stored as a sum of impulses?•10 minutes
  • 4.2.6: Summary of "How to approximate multidimensional integrals efficiently" module plus next steps•3 minutes
6 readings•Total 60 minutes
  • Notes for Lesson 4.2.1•10 minutes
  • Notes for Lesson 4.2.2•10 minutes
  • Notes for Lesson 4.2.3•10 minutes
  • Notes for Lesson 4.2.4•10 minutes
  • Notes for Lesson 4.2.5•10 minutes
  • Notes for Lesson 4.2.6•10 minutes
6 assignments•Total 80 minutes
  • Graded assignment for week 2•30 minutes
  • Practice assignment for Lesson 4.2.1•10 minutes
  • Practice assignment for Lesson 4.2.2•10 minutes
  • Practice assignment for Lesson 4.2.3•10 minutes
  • Practice assignment for Lesson 4.2.4•10 minutes
  • Practice assignment for Lesson 4.2.5•10 minutes
4 ungraded labs•Total 120 minutes
  • Jupyter notebook to experiment with Monte-Carlo method•30 minutes
  • Jupyter notebook to experiment with importance sampling•30 minutes
  • Jupyter notebook to illustrate weight normalization•30 minutes
  • Jupyter notebook to visualize pdfs from weighted impulses•30 minutes

This week, you will put all of the tricks from week two together to implement (and then refine) the particle-filter method.

What's included

7 videos7 readings7 assignments4 ungraded labs

7 videos•Total 114 minutes
  • 4.3.1: Sequential importance sampling (the particle filter)•33 minutes
  • 4.3.2: Setting up an example of the particle filter•15 minutes
  • 4.3.3: Octave code to implement a particle filter•18 minutes
  • 4.3.4: Examining the variables of the basic SIS algorithm•13 minutes
  • 4.3.5: How to "resample" the particles to reduce redundancy•18 minutes
  • 4.3.6: Implementing resampling in Octave; revisiting example•14 minutes
  • 4.3.7: Summary of "Developing and refining the particle-filter algorithm" module plus next steps•3 minutes
7 readings•Total 70 minutes
  • Notes for Lesson 4.3.1•10 minutes
  • Notes for Lesson 4.3.2•10 minutes
  • Notes for Lesson 4.3.3•10 minutes
  • Notes for Lesson 4.3.4•10 minutes
  • Notes for Lesson 4.3.5•10 minutes
  • Notes for Lesson 4.3.6•10 minutes
  • Notes for Lesson 4.3.7•10 minutes
7 assignments•Total 90 minutes
  • Graded assignment for week 3•30 minutes
  • Practice assignment for Lesson 4.3.1•10 minutes
  • Practice assignment for Lesson 4.3.2•10 minutes
  • Practice assignment for Lesson 4.3.3•10 minutes
  • Practice assignment for Lesson 4.3.4•10 minutes
  • Practice assignment for Lesson 4.3.5•10 minutes
  • Practice assignment for Lesson 4.3.6•10 minutes
4 ungraded labs•Total 120 minutes
  • Jupyter notebook to implement SIS method•30 minutes
  • Jupyter notebook to diagnose problem with SIS method•30 minutes
  • Jupyter notebook to illustrate need for resampling•30 minutes
  • Jupyter notebook implementing SIS with resampling•30 minutes

This week, you will learn how to apply the particle filter to an indoor navigation problem.

What's included

6 videos6 readings6 assignments1 ungraded lab

6 videos•Total 87 minutes
  • 4.4.1: Concepts in navigation•17 minutes
  • 4.4.2: The indoor navigation problem•15 minutes
  • 4.4.3: Setting up a sensor model for an example•20 minutes
  • 4.4.4: Setting up pdfs for the example•13 minutes
  • 4.4.5: Implementing indoor navigation using a particle filter in Octave•19 minutes
  • 4.4.6: Summary of "Navigation application using a particle filter" module plus next steps•3 minutes
6 readings•Total 60 minutes
  • Notes for Lesson 4.4.1•10 minutes
  • Notes for Lesson 4.4.2•10 minutes
  • Notes for Lesson 4.4.3•10 minutes
  • Notes for Lesson 4.4.4•10 minutes
  • Notes for Lesson 4.4.5•10 minutes
  • Notes for Lesson 4.4.6•10 minutes
6 assignments•Total 80 minutes
  • Graded assignment for week 4•30 minutes
  • Practice assignment for Lesson 4.4.1•10 minutes
  • Practice assignment for Lesson 4.4.2•10 minutes
  • Practice assignment for Lesson 4.4.3•10 minutes
  • Practice assignment for Lesson 4.4.4•10 minutes
  • Practice assignment for Lesson 4.4.5•10 minutes
1 ungraded lab•Total 30 minutes
  • Jupyter notebook implementing a particle filter for indoor navigation•30 minutes

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Instructor

University of Colorado System
9 Courses•85,362 learners

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