Block 3 — Optimal Fusion

Block 03~45 min1 demo

Combines two independent estimates of the same scalar quantity using inverse-variance weighting, proves the resulting fused variance is always smaller than either input, and motivates the move to recursive estimators.

What you’ll learn

1. Explain why combining sensors improves a navigation estimate.

2. Compute a fused estimate using weighted averaging.

3. Show that the optimal weights depend on the measurement uncertainty of each sensor.

4. Compute the variance of a fused estimate and recognize that it is always smaller than either input variance.

5. See that the Kalman filter is just recursive optimal fusion.

In this block

Reading

Reading

Inverse-variance weighting and the fused variance identity.

Flashcards

Flashcards

Click-to-reveal cards for the block's key terms and equations.

Demo

Demo · Optimal Fusion

Mix radar and baro altitude with adjustable variances and watch the fused PDF tighten.