Explore the fundamentals of Kalman Filters in this comprehensive video lecture, the first part of a three-part series. Begin with an introduction to recursive filters, including average, moving average, and low-pass filters. Learn through practical MATLAB examples that demonstrate real-world applications. Gain insights into the basics of the Kalman Filter algorithm and its importance in estimation and data analysis. Discover how to apply these concepts to dynamic attitude estimation using gyroscope data. Access accompanying MATLAB code and lecture notes to enhance your learning experience. Perfect for beginners looking to build a strong foundation in Kalman Filters and their applications in aerospace engineering and beyond.
Kalman Filter for Beginners - Recursive Filters & MATLAB Examples
Overview
Syllabus
Introduction
Recursive expression for average
Simple example of recursive average filter
MATLAB demo of recursive average filter for noisy data
Moving average filter
MATLAB moving average filter example
Low-pass filter
MATLAB low-pass filter example
Basics of the Kalman Filter algorithm
Taught by
Ross Dynamics Lab
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Reviews
5.0 rating, based on 4 Class Central reviews
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The course covers key filtering techniques and introduces the Kalman Filter algorithm. The course began with a focus on the basics of recursive filtering, starting with the concept of calculating averages recursively. It was fascinating to see how this method eliminates the need to store large datasets, making it ideal for real-time applications.
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The course provides a clear and practical introduction to recursive filters and the Kalman Filter using MATLAB examples.
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Teklebrhan Aregwi Weldegebrealit very precise and good lecture with very short time. it helps to understand the basics behind kalman filter. -
Well Explained. It was very engaging. Highly recommended.The professor is very much experienced in this field.
