courseraИнженерия

Kalman Filter Boot Camp (and State Estimation)

Курс от University of Colorado System

Средний≈ 22.1 чАнглийский

О курсе

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.

Навыки, которые вы освоите

Mathematical Modeling Linear Algebra Probability & Statistics Estimation Control Systems Simulations Simulation and Simulation Software Statistical Methods Probability Probability Distribution Matlab Statistical Modeling

Программа курса

4 модулей · 98 учебных материалов

01What is the purpose of a Kalman filter?24 материалов

1.0: Introduction to the specialization

Frequently Asked QuestionsЧтение Course ResourcesЧтение How to Use Discussion ForumsЧтение Introduce YourselfОбсуждение

Учитесь у экспертов

Gregory Plett

Professor

Kalman Filter Boot Camp (and State Estimation)

О курсе

Навыки, которые вы освоите

Программа курса

1.0: Introduction to the specialization

Учитесь у экспертов

Gregory Plett

1.1.1: Welcome to the course!

1.1.2: What are some key Kalman-filter concepts?

1.1.3: Working through a Kalman-filter example at a high level

1.1.4: Roadmap to this course; context within the specialization

1.1.5: What are some applications that use Kalman filters?

1.1.6: Summary of "What is the Purpose of a Kalman Filter?" module plus next steps

1.2.1: What is a state-space model and why do I need to know about them?

1.2.2: Example continuous-time state-space models used for tracking applications

1.2.3: Understanding the time-domain response of a state-space model

1.2.4: Illustrating the time-domain response

1.2.5: Converting continuous-time state-space models to discrete-time

1.2.6: How do I simulate a discrete-time state-space model?

1.2.7: Is it even possible for a Kalman filter to estimate this model's state?

1.2.8: Summary of "What do I need to know about state-space models?" module plus next steps

1.3.1: Understanding uncertainty via mean and covariance

1.3.2: Understanding joint uncertainty of two unknown quantities

1.3.3: Understanding time-varying uncertain quantities

1.3.4: Simulating correlated Gaussian random vectors

1.3.5: Discrete-time dynamic systems having random inputs

1.3.6: Continuous-time dynamic systems having random inputs

1.3.7: Relating SigmaW to Sw precisely; a little trick (also, relating SigmaV to Sv)

1.3.8: Summary of "What do I need to know about random variables?" module plus next steps

1.4.1: What are the linear Kalman-filter steps?

1.4.2: Preparing a model for use with the linear Kalman filter

1.4.3: How do I implement the Kalman-filter steps in Octave?

1.4.4: More Kalman-filter examples for state estimation of a linear system

1.4.5: What can cause a Kalman filter to fail?

1.4.6: Summary of "State-estimation application of a Kalman filter" module plus next steps