% Generate measurements t = 0:0.1:10; x_true = sin(t); y_true = cos(t); z = [x_true + randn(size(t)); y_true + randn(size(t))];
: Introduces the fundamental logic of updating an estimate with new data without storing old values. It covers Average Filters Moving Averages Low-pass Filters as the building blocks for more complex estimation. Part II: The Kalman Filter Theory : Breaks down the algorithm into its two primary phases: Prediction (Propagation) % Generate measurements t = 0:0
. It doesn't just look at the latest sensor reading; it combines a mathematical prediction of where a system be with a noisy measurement of where it Recursive Processing It doesn't just look at the latest sensor
Kalman Filter for Beginners: with MATLAB Examples - Amazon.com a robot turning in a circle).
Essential for real-world robotics because most systems are non-linear (e.g., a robot turning in a circle).