1D Kalman Filter

Key Insight

Fusing multiple noisy sensors mathematically yields a far more accurate estimate than any single sensor can provide. By using a Kalman Filter (KF) to combine two thermometer readings, the joint estimate's uncertainty shrinks below both individual sensor variances. This covariance contraction demonstrates that the filter is not just averaging the data, but actively extracting certain information from uncertainty.