MATLAB/Simulink implementation of Kalman filters and its non-linear variants
- Kalman Filter is an optimal state observer
- Also called Linear Quadratic Estimation (LQE)
- Works for linear systems
- Takes into account statistical noise
- Combines estimated and measured readings from different sources using joint probability distribution to estimate an optimal reading
Process noise (wk): Noise due to inexact nature of modelled physical equations, such as deviations due to air pressure. Needs to be tuned. Measurement noise (vk): Noise characteristic to the sensor's working. Needs to be obtained from sensor calibration.
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Both wk and vk can be assumed to be mutually uncorrelated white Gaussian noise processes.
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The state-space equations of the system are:
- In control theory, a state observer or state estimator is a system that provides an estimate of the internal state of a given real system, from measurements of the input and output of the real system.
- In essence, Kalman filter has two steps: Predict and Update
Linear model
Non-linear model
Kalman filter
Extended kalman filter
Unscented kalman filter
- Tuning of kalman filter parameters and initial state assumptions
- Normalized error visualization
- Implementation of particle filter for non-gaussian system
- Model a real system and compare results with actual hardware testing
- Battery pack state of charge estimation using ESC cell model
- Sensorless motor drive control and state estimation
- Automotive navigation systems and sensor fusion
- Image processing and noise removal
- https://in.mathworks.com/help/control/ug/extended-and-unscented-kalman-filter-algorithms-for-online-state-estimation.html
- https://groups.seas.harvard.edu/courses/cs281/papers/unscented.pdf
- https://in.mathworks.com/help/control/ug/nonlinear-state-estimation-using-unscented-kalman-filter.html
- https://academic.csuohio.edu/simond/pubs/ESDNonlinear.pdf
- https://ieeexplore.ieee.org/document/7530343
- https://ieeexplore.ieee.org/document/7734116