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