Builds the predict-update cycle in one dimension, frames the Kalman gain as a trust factor between prediction uncertainty and measurement uncertainty, and walks through innovation, gain, and covariance updates on a 1D train example.
## What you'll learn 1. Explain how the Kalman filter performs **recursive optimal fusion**. 2. Identify the roles of **prediction**, **measurement**, and **innovation**. 3. Interpret the **Kalman gain** as an uncertainty-weighted trust factor. 4. Update both the **state** and its **uncertainty** at every step. 5. Run the full predict-update cycle on a one-dimensional example. ## In this block