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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/kalman-filter-boot-camp-state-estimation
课程评论：没有评论
《卡尔曼滤波器训练营（状态估计）》课程简介： 本课程介绍卡尔曼滤波器作为一种解决动态系统内部隐藏状态估计问题的方法。课程主要发展状态空间模型和随机系统的理论背景，详细阐述线性卡尔曼滤波器的步骤，并展示如何在Octave代码中实现这些步骤以及评估滤波器的输出。 课程大纲包括以下几个部分： 1. **卡尔曼滤波器的目的是什么？** 本周，您将学习卡尔曼滤波器的基本概念及其功能。课程将概述整个课程和专业化的路线图，并介绍使用卡尔曼滤波器的应用案例。 2. **关于状态空间模型需要了解些什么？** 卡尔曼滤波器用于估计由“状态空间模型”描述的系统状态。本周，您将学习实现卡尔曼滤波器所需的状态空间模型的背景概念。 3. **关于随机变量需要了解些什么？** 需要估计的系统状态受到未知输入（“干扰”或“过程噪声”）的影响，同时其测量也会受到传感器噪声的影响。这些噪声通过随机变量建模。本周，您将学习实现卡尔曼滤波器所需的随机变量背景知识。 4. **卡尔曼滤波器的状态估计应用** 尽管尚未推导卡尔曼滤波器的步骤，但通过观察卡尔曼滤波器的运行，可以获得其操作的洞察力。本周，您将学习如何在Octave中实现卡尔曼滤波器，并观察其成功的案例以及失败的案例（下一课程中，您将学习原因！）。 本课程适合希望深入了解卡尔曼滤波器及其应用的学习者，通过理论与实践结合，提升其在动态系统状态估计方面的能力。
Name:What is the purpose of a Kalman filter?
Description:This week, you will learn what a Kalman filter is and generally what it does. You will be introduced to the roadmap for the course and the specialization, and will learn some applications that use Kalman filters.
Name:What do I need to know about state-space models?
Description:Kalman filters estimate the "state" of a system that is described using a "state-space model." This week, you will learn the background concepts in state-space models that are required in order to implement a Kalman filter.
Name:What do I need to know about random variables?
Description:Systems whose state we would like to estimate are affected by unknown inputs ("disturbances" or "process noises") and their measurements are affected by sensor noises. These noises are modeled by random variables. This week, you will learn the background concepts in random variables that are required in order to implement a Kalman filter.
Name:State-estimation application of a Kalman filter
Description:Even though we have not yet derived the steps of the Kalman filter, it is instructive to gain insight into a Kalman filter's operation by watching it run. This week, you will learn how to implement a Kalman filter in Octave and see cases where it works well and where it fails (next course, you will learn why!).
Introduces the Kalman filter as a method that can solve problems related to estimating the hidden internal state of a dynamic system. Develops the background theoretical topics in state-space models and stochastic systems. Presents the steps of the linear Kalman filter and shows how to implement these steps in Octave code and how to evaluate the filter’s output.
