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.
Particle Filters (and Navigation)
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Particle Filters (and Navigation)
This course is part of Applied Kalman Filtering Specialization
Instructor: Gregory Plett
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
23 hours to complete
3 weeks at 7 hours a week
Flexible schedule
Learn at your own pace
Skills you'll gain
Details to know
Shareable certificate
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Recently updated!
September 2024
Assessments
28 assignments
Taught in English
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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
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
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
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
This week, you will learn how to apply the particle filter to an indoor navigation problem.
What's included
6 videos6 readings10 assignments1 ungraded lab
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