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rlabbe/Kalman-and-Bayesian-Filters-in-Python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
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In Chapter 3 in the last sentence of the first paragraph of the section Total Probability Theorem i think it should say posterior instead of prior. The probability of being at any position ๐‘– at time ๐‘ก can be written as ๐‘ƒ(๐‘‹๐‘ก๐‘–) . We computed that as the sum of the prior at time ๐‘กโˆ’1 ๐‘ƒ(๐‘‹๐‘กโˆ’1๐‘—)...
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First thank you for your amazing book. I went through most of it and it had been of invaluable help. I was wondering if you had some advice when dealing with non normal process or sensor noise. You always model noise as discrete white noise. How would you adapt the UKF with a noise that is following...
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In Kalman_filter.update(), dot(PHT,self.SI) does not work because PHT is 21 whilst self.SI is 11. so dot can not compute these two matrixs with different dimensions. Therefore, we should change it to PHT*self.SI. This is also true of other functions such as dot(self.K,self.y)