For Educators · State Estimator
State Estimator for a balancing bot
Built for educators teaching it to a class. Drop a live demo into a lecture or assign it as a shareable link — no lab installs. Simulate a balancing bot live below — adjust the inputs and watch it respond, right in your browser.
Kalman FilterLive
optimal state estimation
Controls
Presets
The Kalman filter fuses a motion model with noisy measurements to track a hidden state optimally. Each step it predicts, then corrects using the Kalman gain — trusting the measurement more when the model is uncertain, and vice versa. It smooths the jittery sensor (gray) into a clean estimate (cyan) that hugs the truth. The math behind GPS, radar, and spacecraft navigation.
Data Inspector
Measurement RMSE0.0
Kalman RMSE0.0
Noise reductionNaN%
Governing equation
Reading this result: Measurement noise dwarfs process noise, so the Kalman gain stays small — the filter leans on its motion model and smooths the jitter hard.
Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.
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Frequently asked questions
- Is this good for educators?
- Yes — this version of "State Estimator for a balancing bot" is framed for educators teaching it to a class. Drop a live demo into a lecture or assign it as a shareable link — no lab installs.
- Do I need to install anything?
- No. It runs in any modern browser, free, with no account required.