courseraИнженерия

Particle Filters (and Navigation)

Курс от University of Colorado System

Средний≈ 21.9 чАнглийский

О курсе

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.

Навыки, которые вы освоите

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

Программа курса

4 модулей · 92 учебных материалов

01A brute-force solution for highly nonlinear systems26 материалов

4.1: Introduction to the course

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

Учитесь у экспертов

Gregory Plett

Professor

Particle Filters (and Navigation)

О курсе

Навыки, которые вы освоите

Программа курса

4.1: Introduction to the course

Учитесь у экспертов

Gregory Plett

4.1.1: Welcome to the course!

4.1.2: Review of key concepts

4.1.3: Developing the integral framework for general Bayesian recursion

4.1.4: How to approximate integrals numerically

4.1.5: Implementing Bayesian inference via numeric integration

4.1.6: Octave code to implement Bayesian inference

4.1.7: Summary of "A brute-force solution for highly nonlinear systems" module plus next steps

4.2.1: Introducing the Monte-Carlo method for approximating an integral

4.2.2: The importance of an importance density

4.2.3: Weight normalization

4.2.4: The impulse function

4.2.5: How to visualize a pdf stored as a sum of impulses?

4.2.6: Summary of "How to approximate multidimensional integrals efficiently" module plus next steps

4.3.1: Sequential importance sampling (the particle filter)

4.3.2: Setting up an example of the particle filter

4.3.3: Octave code to implement a particle filter

4.3.4: Examining the variables of the basic SIS algorithm

4.3.5: How to "resample" the particles to reduce redundancy

4.3.6: Implementing resampling in Octave; revisiting example

4.3.7: Summary of "Developing and refining the particle-filter algorithm" module plus next steps

4.4.1: Concepts in navigation

4.4.2: The indoor navigation problem

4.4.3: Setting up a sensor model for an example

4.4.4: Setting up pdfs for the example

4.4.5: Implementing indoor navigation using a particle filter in Octave

4.4.6: Summary of "Navigation application using a particle filter" module plus next steps