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Particle Filters (and Navigation)

Particle Filters (and Navigation)

本课程是 Applied Kalman Filtering 专项课程的一部分

位教师：Gregory Plett

包含在中

了解更多

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将获得的技能

您将学习的工具

Matlab

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

24 项作业

授课语言：英语（English）

91% of learners achieved a positive career outcome

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本课程是 Applied Kalman Filtering 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

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该课程共有4个模块

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.

单元详情

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.

涵盖的内容

7个视频12篇阅读材料5个作业1个讨论话题1个非评分实验室

7个视频总计103分钟

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

12篇阅读材料总计120分钟

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

5个作业总计70分钟

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

1个讨论话题总计10分钟

Introduce yourself10分钟

1个非评分实验室总计30分钟

Jupyter notebook implementing brute-force Bayesian inference30分钟

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

涵盖的内容

6个视频6篇阅读材料6个作业4个非评分实验室

6个视频总计71分钟

4.2.1: Introducing the Monte-Carlo method for approximating an integral13分钟
4.2.2: The importance of an importance density12分钟
4.2.3: Weight normalization17分钟
4.2.4: The impulse function15分钟
4.2.5: How to visualize a pdf stored as a sum of impulses?10分钟
4.2.6: Summary of "How to approximate multidimensional integrals efficiently" module plus next steps3分钟

6篇阅读材料总计60分钟

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

6个作业总计80分钟

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

4个非评分实验室总计120分钟

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

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

涵盖的内容

7个视频7篇阅读材料7个作业4个非评分实验室

7个视频总计114分钟

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

7篇阅读材料总计70分钟

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

7个作业总计90分钟

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

4个非评分实验室总计120分钟

Jupyter notebook to implement SIS method30分钟
Jupyter notebook to diagnose problem with SIS method30分钟
Jupyter notebook to illustrate need for resampling30分钟
Jupyter notebook implementing SIS with resampling30分钟

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

涵盖的内容

6个视频6篇阅读材料6个作业1个非评分实验室

6个视频总计87分钟

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