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In this course, you will learn:
- Recursive estimating theory and the fundamentals of Bayesian statistics
- Describe and model common sensors, as well as the measurements they produce.
- Compare common positional motion models to see when they should be employed in real-world situations.
- Describe the Kalman filter's (KF) basic features and how to use it on linear state space models.
- To handle problems involving nonlinear motion and/or sensor models, use Matlab to implement crucial nonlinear filters.
- Analyze the properties and requirements of an application to find a good filter mechanism.
Syllabus:
- Introduction and Primer in statistics
- Bayesian statistics
- State-space models and optimal filters
- The Kalman filter and its properties
- Motion and measurements models
- Non-linear filtering
- Particle filter
