Applied Kalman Filtering

Overview

In this specialization, you will learn how to derive, design, and implement Kalman-filter solutions to common engineering problems. You will be able to develop linear and nonlinear Kalman filters and particle filters in Octave code and debug and correct anomalous behaviors.

Syllabus

Course 1: Kalman Filter Boot Camp (and State Estimation)
Course 2: Linear Kalman Filter Deep Dive (and Target Tracking)
Course 3: Nonlinear Kalman Filters (and Parameter Estimation)
Course 4: Particle Filters (and Navigation)

Courses

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22 hours 5 minutes
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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.
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21 hours 43 minutes
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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.
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21 hours 7 minutes
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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.
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21 hours 55 minutes
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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.