This algorithm is a recursive two-step process: prediction, and update. The prediction step produces estimates of the current variables along with their uncertainties. These estimates are based on the assumed model of how the estimates change over time. The update step is done when the next measurements (subject to noise) is observed. In this step, the estimates (let’s call it state from here on) are updated based on the weighted average of the predicted state and the state based on the current measurement. A lower weight is given to that with a higher uncertainty.
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