Kalman filter - summary of optimal state estimation knowledge (1)

Combing of the mind map based on the course of Can teacher at station b;
https://www.bilibili.com/video/BV1dV411B7ME/?spm_id_from=333.788
1. Recursive algorithm: optimize recursive digital processing algorithm
2. Mathematical basis: data Fusion; covariance matrix; observer;
3. Kalman gain hyper-detailed mathematical derivation
4. Error covariance matrix; Kalman's complete five formulas

5 Extended Kalman filter ekf: nonlinear system; linear expression in Kalman;
so it needs to be linearized
Fundamentally: to linearize a system, you need to find a point x0 and perform linearization around it
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For a more detailed summary of these courses, please refer to the notes made by other friends:
https://blog.csdn.net/py431382/article/details/109854357

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Origin blog.csdn.net/tangpingping__/article/details/123471977