With Matlab Examples Download ((better)) Top — Kalman Filter For Beginners
% Generate true positions true_pos = real_position + real_velocity * t;
This is the fundamental problem of . Every measurement we take from the real world is corrupted by noise. If we rely on a single sensor, we get jittery, unreliable data. If we rely solely on a mathematical model, we drift away from reality over time. % Generate true positions true_pos = real_position +
% --- 3. INITIALIZE THE KALMAN FILTER --- % State vector: [position; velocity] x_est = [0; 5]; % Initial guess (position 0, velocity 5 m/s) P_est = [10, 0; 0, 5]; % Initial uncertainty (high uncertainty) If we rely solely on a mathematical model,
% Model matrices (Constant velocity) F = [1, dt; 0, 1]; % State transition matrix H = [1, 0]; % Measurement matrix (we only measure position) Q = [0.01, 0; 0, 0.01]; % Process noise (small, trust model) R = measurement_noise_std^2; % Measurement noise (variance) In one month, you will track a moving object from a webcam
By the end of this week, you will understand the five equations. In one month, you will track a moving object from a webcam. In one year, you will build a sensor-fusion drone.
Happy filtering! %% Kalman Filter Loop Template (MATLAB) for k = 1:length(measurements) % Predict x_pred = F * x_est; P_pred = F * P_est * F' + Q; % Update K = P_pred * H' / (H * P_pred * H' + R); x_est = x_pred + K * (measurements(k) - H * x_pred); P_est = (eye(size(P_pred)) - K * H) * P_pred; end
