1. Understand the common scenarios of using Kalman filter through several cases. The Kalman filter is an optimal estimation algorithm that estimates system states under uncertainty and indirect measurements. Watch the video example to learn how the Kalman filter works.
2. This video introduces the working principle of the state observer and explains the mathematical principles behind it. State observers are used to estimate the internal state of the system when direct measurement is not possible.
3. This video introduces the Kalman filter to combine two sources of information, the predicted state and the noise measurement, to produce an optimal, unbiased state estimate
4. This video discusses the system of equations required to implement the Kalman filter algorithm.
5. This video explains the basic concepts behind nonlinear state estimators, including extended Kalman filter, unscented Kalman filter and particle filter.
6. In this video you will learn how to configure the Kalman filter module parameters, such as system model, initial state estimation and noise characteristics, and estimate the pendulum model angle using the Kalman filter in Simulink.
7. This video demonstrates the use of the extended Kalman filter to estimate the angular position of a nonlinear pendulum system. You will learn how to configure the Extended Kalman Filter block parameters, such as state transitions and measurement functions, and generate C/C++ code.
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