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.

Spezialisierung „Applied Kalman Filtering“

spezialisierung ist nicht verfügbar in Deutsch (Deutschland)

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Spezialisierung „Applied Kalman Filtering“

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

Dozent: Gregory Plett

2.772 bereits angemeldet

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4-teilige Kursreihe

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Empfohlene Erfahrung

4 months to complete

unter 5 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

4-teilige Kursreihe

Befassen Sie sich eingehend mit einem Thema

aus 38 Bewertungen von Kursen in diesem Programm

Stufe Mittel

Empfohlene Erfahrung

4 months to complete

unter 5 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

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

Kompetenzen, die Sie erwerben

Kategorie: Simulation and Simulation Software
Kategorie: Mathematical Modeling
Kategorie: Time Series Analysis and Forecasting
Kategorie: Statistical Modeling
Kategorie: Bayesian Network
Kategorie: Probability & Statistics
Kategorie: Forecasting
Kategorie: Probability
Kategorie: Markov Model
Kategorie: Correlation Analysis
Kategorie: Estimation
Kategorie: Simulations
Kategorie: Probability Distribution
Kategorie: Control Systems
Kategorie: Statistical Methods
Kategorie: Applied Mathematics
Kategorie: Numerical Analysis
Kategorie: Integral Calculus
Kategorie: Linear Algebra

Werkzeuge, die Sie lernen werden

Kategorie: Matlab

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Spezialisierung - 4 Kursreihen

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.

Übungsprojekt

Learners will start with provided code templates (in the Octave/MATLAB language) to develop solutions for state estimation, target tracking, parameter estimation, and navigation problems. An emphasis on a detailed fundamental background enables implementations that are robust and efficient.

Kalman Filter Boot Camp (and State Estimation)

KURS 1, 22 Stunden

Was Sie lernen werden

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.

Kompetenzen, die Sie erwerben

Kategorie: Mathematical Modeling

Kategorie: Linear Algebra

Kategorie: Estimation

Kategorie: Probability & Statistics

Kategorie: Control Systems

Kategorie: Probability Distribution

Kategorie: Simulations

Kategorie: Simulation and Simulation Software

Kategorie: Statistical Methods

Kategorie: Matlab

Kategorie: Statistical Modeling

Kategorie: Probability

Linear Kalman Filter Deep Dive (and Target Tracking)

KURS 2, 22 Stunden

Was Sie lernen werden

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.

Kompetenzen, die Sie erwerben

Kategorie: Estimation

Kategorie: Applied Mathematics

Kategorie: Mathematical Modeling

Kategorie: Linear Algebra

Kategorie: Statistical Methods

Kategorie: Matlab

Kategorie: Forecasting

Kategorie: Numerical Analysis

Kategorie: Statistical Inference

Kategorie: Time Series Analysis and Forecasting

Kategorie: Correlation Analysis

Kategorie: Markov Model

Nonlinear Kalman Filters (and Parameter Estimation)

KURS 3, 21 Stunden

Was Sie lernen werden

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.

Kompetenzen, die Sie erwerben

Kategorie: Estimation

Kategorie: System Implementation

Kategorie: Matlab

Kategorie: Applied Mathematics

Kategorie: Statistical Methods

Kategorie: Calculus

Kategorie: Numerical Analysis

Kategorie: Derivatives

Kategorie: Simulation and Simulation Software

Kategorie: Control Systems

Kategorie: Mathematical Modeling

Kategorie: Mathematical Software

Kategorie: Model Evaluation

Particle Filters (and Navigation)

KURS 4, 22 Stunden

Was Sie lernen werden

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.