The Kalman Filter: An algorithm for making sense of fused sensor insight
"The Kalman filter is relatively quick and easy to implement and provides an optimal estimate of the condition for normally distributed noisy sensor values under certain conditions. Mr. Kalman was so convinced of his algorithm that he was able to inspire a friendly engineer at NASA. And so this filter helped for the first time in the Apollo Guidance Computer at the moon landings ."
Kalman Filter
"In statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, one of the primary developers of its theory."
https://en.wikipedia.org/wiki/Kalman_filter
Kalman Filter in Two Dimensions
https://www.researchgate.net/publication/3082925_Kalman_filtering_in_two_dimensions
Understanding and Applying Kalman Filtering
http://biorobotics.ri.cmu.edu/papers/sbp_papers/integrated3/kleeman_kalman_basics.pdf
Related:
Click to access 1910.03558.pdf
Click to access tutorialEKF.pdf
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Sayed Ahmed
BSc. Eng. in Comp. Sc. & Eng. (BUET)
MSc. in Comp. Sc. (U of Manitoba, Canada)
MSc. in Data Science and Analytics (Ryerson University, Canada)
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