Kalman Filter Sensor Fusion: Noisy Data to State

A Kalman filter is a disciplined way to answer a practical question: what is the best current state estimate when every sensor is noisy? It predicts where the state should be, then corrects that prediction when a new measurement arrives.

The Kalman Filter Sensor Fusion Lab makes that loop visible. It draws the true path, noisy measurements and filtered estimate, then exports the same run as SVG, CSV, Markdown and JSON.

The Core Loop

The prediction step moves the state forward with a transition matrix. The correction step compares the prediction with a measurement, computes an innovation residual and uses the Kalman gain to decide how strongly to trust the new observation.

Why RMSE Is Not Enough

RMSE shows whether the filtered path is better than raw measurements, but a serious diagnostic also needs innovation and covariance. Innovation shows how surprising the measurements were. Covariance shows how uncertain the estimate became.

How To Use The Lab

1. Open the Kalman Filter Sensor Fusion Lab.
2. Run the robot sample first.
3. Increase position noise and watch raw measurements drift.
4. Change process noise and measurement noise to tune trust.
5. Export estimate CSV and measurement CSV when you need to inspect the run.

What This Does Not Prove

This is deterministic browser math for planning, teaching and debugging. It is not navigation certification, medical advice, legal advice, financial advice, safety-critical control, traffic proof, ranking proof, revenue proof or AdSense approval proof.