The math behind GPS, radar, and spacecraft navigation. A Kalman filter blends a prediction with a noisy measurement to track a hidden state better than either alone.
Kalman FilterLive
optimal state estimation
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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.
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.
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How it works
The Kalman filter alternates predict and update steps, maintaining an estimate and its uncertainty. The Kalman gain optimally weights the new measurement against the model prediction — trusting the sensor more when the model is uncertain, and vice versa. It turns a jittery sensor stream into a smooth, accurate track, the workhorse of estimation and sensor fusion.
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Is this Kalman filter simulator tool really free?▾
Yes. Kalman Filter runs entirely in your browser using your device's own compute, so local use is free forever. You only pay Compute Tokens if you scale a job to the cloud.
Do I need to install anything?▾
No. Everything runs client-side in a modern browser — no downloads, no license, no account required to start.
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How accurate are the results?▾
The solver uses established numerical methods, but results are for research and educational purposes and should be validated against experiment or professional review before you rely on them.