Kalman Filter For Beginners With Matlab Examples Pdf Guide

% Initial state x_true = [0; 1]; % start at 0, velocity 1 x_hat = [0; 0]; % initial guess P = eye(2); % initial uncertainty

The Kalman filter smooths the noisy measurements and gives a much cleaner position estimate. 6. MATLAB Example 2 – Understanding the Kalman Gain % Show how Kalman gain changes with measurement noise clear; clc; dt = 1; A = [1 dt; 0 1]; H = [1 0]; kalman filter for beginners with matlab examples pdf

% Noise covariances Q = [0.01 0; 0 0.01]; % process noise (small) R = 1; % measurement noise (variance) % Initial state x_true = [0; 1]; %

% Update K = P_pred * H' / (H * P_pred * H' + R); x_hat = x_pred + K * (measurements(k) - H * x_pred); P = (eye(2) - K * H) * P_pred; % Initial state x_true = [0

% Generate noisy measurements num_steps = 50; measurements = zeros(1, num_steps); for k = 1:num_steps x_true = A * x_true; % true motion measurements(k) = H * x_true + sqrt(R)*randn; % noisy measurement end