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Particle Filters (and Navigation)
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Particle Filters (and Navigation)

Master particle filter implementations for nonlinear state estimation, with applications in indoor navigation and Bayesian inference.

Course Cost

Free course

Intermediate

Skill Level

20 Hours

Self-paced lessons

This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Applied Kalman Filtering Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.

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What you'll learn

  • Implement robust particle-filter algorithms in Octave

  • Apply Monte-Carlo integration methods effectively

  • Develop sequential importance sampling techniques

  • Solve indoor navigation problems using particle filters

  • Implement Bayesian inference state-estimation solutions

Skills you'll gain

Particle Filters
Monte Carlo Integration
Bayesian Inference
State Estimation
Navigation Systems
Octave Programming
Sequential Importance Sampling
Indoor Navigation
System Modeling
Algorithm Implementation

This course includes:

6.23 Hours PreRecorded video

2 quizzes, 26 assignments

Access on Mobile, Tablet, Desktop

FullTime access

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There are 4 modules in this course

This advanced course focuses on developing particle filters for solving strongly nonlinear state-estimation problems. Students learn Monte-Carlo integration, importance density concepts, and sequential importance sampling methods for estimating posterior probability density functions. The curriculum covers implementation of robust particle filters in Octave, with practical applications in indoor navigation. Through hands-on programming exercises and theoretical study, students master both the mathematical foundations and practical applications of particle filtering techniques.

A brute-force solution for highly nonlinear systems

Module 1 · 5 Hours to complete

How to approximate multidimensional integrals efficiently

Module 2 · 5 Hours to complete

Developing and refining the particle-filter algorithm

Module 3 · 6 Hours to complete

Navigation application using a particle filter

Module 4 · 6 Hours to complete

Fee Structure

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

Particle Filters (and Navigation)

Intermediate

Skill Level

20 Hours

Self-paced lessons

Course Cost

Free course

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.