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Interactive 3D/Kalman Filter
Noise Parameters
Process noise Q0.10
High Q = state changes fast
Obs. noise R2.00
High R = noisy sensor
Initial P₀1.00
Speed
Delay100ms
Key Insight
K = P⁻/(P⁻+R). When R is small (accurate sensor), K→1 and we trust the measurement. When Q is small (stable system), K→0 and we trust our prediction.

Kalman Filter - Interactive Visualization

The Kalman filter is optimal linear state estimation. It alternates: predict (advance state estimate using dynamics model) and correct (update estimate using noisy observation). The Kalman gain K trades off process noise Q vs observation noise R. High Q (uncertain dynamics): trust observations more. High R (noisy sensors): trust the model more. This visualization tracks a 1D position estimate.

  • Step through predict and correct cycles one at a time
  • See true position, noisy observations, and Kalman estimate together
  • Adjust process noise Q and observation noise R to change Kalman gain
  • Watch uncertainty bands update: narrow after corrections, widen during predictions
  • Foundation for robot localization, financial filtering, and sensor fusion

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.