中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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24 项作业
授课语言：英语（English）
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本课程是 Applied Kalman Filtering 专项课程 专项课程的一部分
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该课程共有4个模块
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
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
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
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
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