Let's consider an example where we want to estimate the position and velocity of an object from noisy measurements of its position and velocity.
Which one do you trust more? The Kalman filter doesn’t choose one; it . If the prediction is uncertain, it trusts the measurement more. If the measurement is noisy, it trusts the prediction more. Over time, it learns the uncertainty and produces estimates that are better than either source alone. kalman filter for beginners with matlab examples download
Think of the Kalman Filter as a . It combines two sources of information to give you a better estimate than either could provide alone: Let's consider an example where we want to
% Define the system parameters dt = 0.1; % time step A = [1 dt; 0 1]; % transition model H = [1 0; 0 1]; % measurement model Q = [0.01 0; 0 0.01]; % process noise R = [0.1 0; 0 0.1]; % measurement noise If the prediction is uncertain, it trusts the