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Particle Filters (and Navigation)

Particle Filters (and Navigation)

This course is part of Applied Kalman Filtering Specialization

Instructor: Gregory Plett

Included with

Learn more

4 modules

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

4 modules

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

Skills you'll gain

Tools you'll learn

Matlab

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

24 assignments

Taught in English

91%

of learners achieved a positive career outcome

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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.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

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.

Module details

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 videosTotal 103 minutes

4.1.1: Welcome to the course!8 minutes
4.1.2: Review of key concepts17 minutes
4.1.3: Developing the integral framework for general Bayesian recursion20 minutes
4.1.4: How to approximate integrals numerically12 minutes
4.1.5: Implementing Bayesian inference via numeric integration18 minutes
4.1.6: Octave code to implement Bayesian inference26 minutes
4.1.7: Summary of "A brute-force solution for highly nonlinear systems" module plus next steps3 minutes

12 readingsTotal 120 minutes

Frequently Asked Questions10 minutes
Course Resources10 minutes
How to Use Discussion Forums10 minutes
Earn a Course Certificate10 minutes
Are you interested in earning an online MSEE degree?10 minutes
Notes for Lesson 4.1.110 minutes
Notes for Lesson 4.1.210 minutes
Notes for Lesson 4.1.310 minutes
Notes for Lesson 4.1.410 minutes
Notes for Lesson 4.1.510 minutes
Notes for Lesson 4.1.610 minutes
Notes for Lesson 4.1.710 minutes

5 assignmentsTotal 70 minutes

Graded assignment for week 130 minutes
Practice assignment for Lesson 4.1.310 minutes
Practice assignment for Lesson 4.1.410 minutes
Practice assignment for Lesson 4.1.510 minutes
Practice assignment for Lesson 4.1.610 minutes

1 discussion promptTotal 10 minutes

Introduce yourself10 minutes

1 ungraded labTotal 30 minutes

Jupyter notebook implementing brute-force Bayesian inference30 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 videosTotal 71 minutes

4.2.1: Introducing the Monte-Carlo method for approximating an integral13 minutes
4.2.2: The importance of an importance density12 minutes
4.2.3: Weight normalization17 minutes
4.2.4: The impulse function15 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 steps3 minutes

6 readingsTotal 60 minutes

Notes for Lesson 4.2.110 minutes
Notes for Lesson 4.2.210 minutes
Notes for Lesson 4.2.310 minutes
Notes for Lesson 4.2.410 minutes
Notes for Lesson 4.2.510 minutes
Notes for Lesson 4.2.610 minutes

6 assignmentsTotal 80 minutes

Graded assignment for week 230 minutes
Practice assignment for Lesson 4.2.110 minutes
Practice assignment for Lesson 4.2.210 minutes
Practice assignment for Lesson 4.2.310 minutes
Practice assignment for Lesson 4.2.410 minutes
Practice assignment for Lesson 4.2.510 minutes

4 ungraded labsTotal 120 minutes

Jupyter notebook to experiment with Monte-Carlo method30 minutes
Jupyter notebook to experiment with importance sampling30 minutes
Jupyter notebook to illustrate weight normalization30 minutes
Jupyter notebook to visualize pdfs from weighted impulses30 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 videosTotal 114 minutes

4.3.1: Sequential importance sampling (the particle filter)33 minutes
4.3.2: Setting up an example of the particle filter15 minutes
4.3.3: Octave code to implement a particle filter18 minutes
4.3.4: Examining the variables of the basic SIS algorithm13 minutes
4.3.5: How to "resample" the particles to reduce redundancy18 minutes
4.3.6: Implementing resampling in Octave; revisiting example14 minutes
4.3.7: Summary of "Developing and refining the particle-filter algorithm" module plus next steps3 minutes

7 readingsTotal 70 minutes

Notes for Lesson 4.3.110 minutes
Notes for Lesson 4.3.210 minutes
Notes for Lesson 4.3.310 minutes
Notes for Lesson 4.3.410 minutes
Notes for Lesson 4.3.510 minutes
Notes for Lesson 4.3.610 minutes
Notes for Lesson 4.3.710 minutes

7 assignmentsTotal 90 minutes

Graded assignment for week 330 minutes
Practice assignment for Lesson 4.3.110 minutes
Practice assignment for Lesson 4.3.210 minutes
Practice assignment for Lesson 4.3.310 minutes
Practice assignment for Lesson 4.3.410 minutes
Practice assignment for Lesson 4.3.510 minutes
Practice assignment for Lesson 4.3.610 minutes

4 ungraded labsTotal 120 minutes

Jupyter notebook to implement SIS method30 minutes
Jupyter notebook to diagnose problem with SIS method30 minutes
Jupyter notebook to illustrate need for resampling30 minutes
Jupyter notebook implementing SIS with resampling30 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 videosTotal 87 minutes

4.4.1: Concepts in navigation17 minutes
4.4.2: The indoor navigation problem15 minutes
4.4.3: Setting up a sensor model for an example20 minutes
4.4.4: Setting up pdfs for the example13 minutes
4.4.5: Implementing indoor navigation using a particle filter in Octave19 minutes
4.4.6: Summary of "Navigation application using a particle filter" module plus next steps3 minutes