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Particle Filters (and Navigation)

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Particle Filters (and Navigation)

Ce cours fait partie de Spécialisation "Applied Kalman Filtering"

Instructeur : Gregory Plett

Inclus avec

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

2 semaines à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

2 semaines à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Compétences que vous acquerrez

Catégorie : Bayesian Network
Catégorie : Markov Model
Catégorie : Mathematical Modeling
Catégorie : Applied Mathematics
Catégorie : Estimation
Catégorie : Numerical Analysis
Catégorie : Engineering Analysis
Catégorie : Integral Calculus
Catégorie : Algorithms
Catégorie : Simulations

Outils que vous découvrirez

Catégorie : Matlab

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

Évaluations

24 devoirs

Enseigné en Anglais

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Élaborez votre expertise du sujet

Ce cours fait partie de la Spécialisation "Applied Kalman Filtering"

Lorsque vous vous inscrivez à ce cours, vous êtes également inscrit(e) à cette Spécialisation.

Apprenez de nouveaux concepts auprès d'experts du secteur
Acquérez une compréhension de base d'un sujet ou d'un outil
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Il y a 4 modules dans ce cours

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.

Détails du module

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.

Inclus

7 vidéos12 lectures5 devoirs1 sujet de discussion1 laboratoire non noté

7 vidéosTotal 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 lecturesTotal 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 devoirsTotal 70 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
Graded assignment for week 130 minutes

1 sujet de discussionTotal 10 minutes

Introduce yourself10 minutes

1 laboratoire non notéTotal 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.

Inclus

6 vidéos6 lectures6 devoirs4 laboratoires non notés

6 vidéosTotal 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 lecturesTotal 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 devoirsTotal 80 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
Graded assignment for week 230 minutes

4 laboratoires non notésTotal 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.

Inclus

7 vidéos7 lectures7 devoirs4 laboratoires non notés

7 vidéosTotal 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 lecturesTotal 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 devoirsTotal 90 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
Graded assignment for week 330 minutes

4 laboratoires non notésTotal 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.

Inclus

6 vidéos6 lectures6 devoirs1 laboratoire non noté

6 vidéosTotal 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

6 lecturesTotal 60 minutes

Notes for Lesson 4.4.110 minutes
Notes for Lesson 4.4.210 minutes
Notes for Lesson 4.4.310 minutes
Notes for Lesson 4.4.410 minutes
Notes for Lesson 4.4.510 minutes
Notes for Lesson 4.4.610 minutes

6 devoirsTotal 80 minutes

Practice assignment for Lesson 4.4.110 minutes
Practice assignment for Lesson 4.4.210 minutes
Practice assignment for Lesson 4.4.310 minutes
Practice assignment for Lesson 4.4.410 minutes
Practice assignment for Lesson 4.4.510 minutes
Graded assignment for week 430 minutes

1 laboratoire non notéTotal 30 minutes

Jupyter notebook implementing a particle filter for indoor navigation30 minutes

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