This article breaks down the core concepts of the Kalman Filter, explains why Phil Kim's approach is highly regarded, and provides practical MATLAB examples to get you started. What is a Kalman Filter?
Suppose we have a scalar state $x$ (e.g., the position of a stationary car). We take a series of measurements $y_k$. Due to sensor noise, $y_k \neq x$. This article breaks down the core concepts of
: Project the current state and error covariance ahead in time using the system model. Kalman gain… wait
“Prediction, update, covariance, Kalman gain… wait, where did that come from?” where did that come from?”