What will I be able to do upon completing the Specialization?

Design and implement robust linear and nonlinear Kalman filters and particle filters to solve important engineering state-estimation problems.

Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Can I take the course for free?

No, you cannot take this course for free. When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you cannot afford the fee, you can apply for financial aid.

Applied Kalman Filtering Specialization

Applied Kalman Filtering Specialization

Learn how to Design and Implement Kalman Filters. Linear and nonlinear Kalman filters and particle filters for state estimation, target tracking, parameter estimation, and navigation

Instructor: Gregory Plett

Included with Coursera Plus

Learn more

4 course series

Get in-depth knowledge of a subject

4.9

(22 reviews)

Intermediate level

Recommended experience

4 months at 5 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

4 course series

Get in-depth knowledge of a subject

4.9

(22 reviews)

Intermediate level

Recommended experience

4 months at 5 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

What you'll learn

How to design and implement robust linear and nonlinear Kalman filters and particle filters to solve important engineering state-estimation problems.

Overview

What’s included

Shareable certificate

Add to your LinkedIn profile

Taught in English

105 practice exercises

Advance your subject-matter expertise

Learn in-demand skills from university and industry experts
Master a subject or tool with hands-on projects
Develop a deep understanding of key concepts
Earn a career certificate from University of Colorado System

Specialization - 4 course series

Kalman Filter Boot Camp (and State Estimation)

Course 122 hours4.9 (17 ratings)View course

What you'll learn

Skills you'll gain

Mathematical Modeling, Linear Algebra, Applied Mathematics, Probability & Statistics, Estimation, Control Systems, Simulations, Statistical Methods, Matlab, Time Series Analysis and Forecasting, Probability Distribution, and Engineering Analysis

Linear Kalman Filter Deep Dive (and Target Tracking)

Course 221 hoursView course

What you'll learn

As a follow-on course to "Kalman Filter Boot Camp", this course derives the steps of the linear Kalman filter to give understanding regarding how to adjust the method to applications that violate the standard assumptions. Applies this understanding to enhancing the robustness of the filter and to extend to applications including prediction and smoothing. Shows how to implement a target-tracking application in Octave code using an interacting multiple-model Kalman filter.

Skills you'll gain

Mathematical Modeling, Applied Mathematics, Linear Algebra, Advanced Mathematics, Time Series Analysis and Forecasting, Systems Of Measurement, Statistical Methods, Predictive Analytics, Probability & Statistics, and Predictive Modeling

Nonlinear Kalman Filters (and Parameter Estimation)

Course 321 hoursView course

What you'll learn

As a follow-on course to "Linear Kalman Filter Deep Dive", this course derives the steps of the extended Kalman filter and the sigma-point Kalman filter for estimating the state of nonlinear dynamic systems. You will learn how to implement these filters in Octave code and compare their results. You will be introduced to adaptive methods to tune Kalman-filter noise-uncertainty covariances online. You will learn how to estimate the parameters of a state-space model using nonlinear Kalman filters.

Skills you'll gain

Estimation, System Implementation, Applied Mathematics, Control Systems, Probability & Statistics, Simulation and Simulation Software, Computer Programming Tools, Numerical Analysis, and Mathematical Modeling

Particle Filters (and Navigation)

Course 421 hoursView course

What you'll learn

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.

Skills you'll gain

Estimation, Applied Mathematics, Numerical Analysis, Computer Programming Tools, Mathematical Modeling, Matlab, Simulations, Engineering Analysis, Bayesian Network, Computational Thinking, Global Positioning Systems, and Algorithms