Why You Should Use The Kalman Filter Tutorial- #Pokemon Example
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Kalman filtering, also known as linear quadratic estimation, 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 precise than those based on a single measurement alone, by using Bayesian inference and 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.

This tutorial breaks down the components of the Kalman filter making easy for anyone to understand. It introduces you to the concepts of the Kalman filter using the pokemon analogy.

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