Data Fusion with Linear Kalman Filter
Theory and Implementation
4.62 (682 reviews)
4 680
students
5.5 hours
content
Dec 2020
last update
$84.99
regular price
What you will learn
How to probabilistically express uncertainty using probability distributions
How to convert differential systems into a state space representation
How to simulate and describe state space dynamic systems
How to use Least Squares Estimation to solve estimation problems
How to use the Linear Kalman Filter to solve optimal estimation problems
How to derive the system matrices for the Kalman Filter in general for any problem
How to optimally tune the Linear Kalman Filter for best performance
How to implement the Linear Kalman Filter in Python
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Our Verdict
Data Fusion with Linear Kalman Filter: Theory and Implementation delivers valuable insights into the world of sensor fusion. It boasts a thorough examination of challenging theory, complemented by hands-on Python examples and exercises that solidify understanding. However, some aspects such as rapid speaking pace, limited exercises, and confusing notations detract from its effectiveness. Despite these shortcomings, this course remains an informative and practical resource for those eager to delve into Linear Kalman Filters, offering a worthwhile learning experience for students proactive in seeking foundational knowledge.
What We Liked
- Comprehensive coverage of Linear Kalman Filter theory and implementation
- Well-organized content with ample information for practical application
- Python used for examples and exercises, enabling hands-on learning
- Clear and well-structured slides with thoughtful explanations
- Prompt responses to questions and doubts
- Realistic examples that facilitate independent thinking
Potential Drawbacks
- Rapid speaking pace can be challenging for first-time learners of Kalman Filters
- Lack of detailed script explanations for KF examples
- Limited number of exercises
- Complex theory could benefit from further clarification and extension
- Confusing variable notations at times
- Built-in code segments in LSE section require improvement
3420416
udemy ID
14/08/2020
course created date
07/01/2021
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