Kalman Filter
Version 1.0.0 · Updated 2026-07-30
CORE DEFINITION
A recursive algorithm that estimates the true state of a system by combining the 'prediction from the previous time step' and the 'current measurement' in the presence of noisy (erroneous) data. It neither fully trusts the prediction nor the measurement, but weights them according to their uncertainties.
SCAFFOLDING EFFECT
Reduce cognitive load
Dynamically correct deviations. In the face of an uncertain future, do not rigidly adhere to the initial plan (prediction), nor blindly follow the flow (measurement). Like the Kalman filter, continuously update the 'best estimate' of the current state based on new information, and approach the truth amidst noise.
Anchor fast decisions
Using Bayesian-style recursion, it weights between prediction (model) and measurement (observation) according to their uncertainties, yielding a state estimate with minimum mean square error; the side with smaller uncertainty gets more weight, dynamically approaching the truth.
MINIMUM ACTION
In progress 0/4Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Kalman_filterverified
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