% Run Kalman filter for i = 1:length(t) % Predict x_pred = A*x_est; P_pred = A*P_est*A' + Q;
A common beginner example is estimating a constant voltage, where the sensor is noisy. % --- Kalman Filter for Constant Voltage Measurement --- % Based on Phil Kim's "Kalman Filter for Beginners" % 1. Simulation Parameters ; true_v = - % True voltage v_noisy = true_v + randn( % Noisy measurements % 2. Initialize Kalman Filter Variables % Initial guess % Initial estimation error covariance (uncertainty) % Process noise covariance (constant, so very low) % Measurement noise covariance (std^2) % To store results estimates = zeros( % 3. Kalman Filter Loop % Prediction x_pred = x; P_pred = P + Q;
% Define system parameters A = [1 1; 0 1]; % state transition matrix H = [1 0]; % measurement matrix Q = [0.01 0; 0 0.01]; % process noise covariance R = 0.1; % measurement noise covariance
% Given functions f(x,u) and h(x) x_hat = x0; P = P0; for k=1:N % Predict x_pred = f(x_hat, u(:,k)); F = jacobian_f(x_hat, u(:,k)); P_pred = F * P * F' + Q;
Based on Laravel 5
Additional features always being planned/researched
"git" involved
% Run Kalman filter for i = 1:length(t) % Predict x_pred = A*x_est; P_pred = A*P_est*A' + Q;
A common beginner example is estimating a constant voltage, where the sensor is noisy. % --- Kalman Filter for Constant Voltage Measurement --- % Based on Phil Kim's "Kalman Filter for Beginners" % 1. Simulation Parameters ; true_v = - % True voltage v_noisy = true_v + randn( % Noisy measurements % 2. Initialize Kalman Filter Variables % Initial guess % Initial estimation error covariance (uncertainty) % Process noise covariance (constant, so very low) % Measurement noise covariance (std^2) % To store results estimates = zeros( % 3. Kalman Filter Loop % Prediction x_pred = x; P_pred = P + Q; % Run Kalman filter for i = 1:length(t)
% Define system parameters A = [1 1; 0 1]; % state transition matrix H = [1 0]; % measurement matrix Q = [0.01 0; 0 0.01]; % process noise covariance R = 0.1; % measurement noise covariance Initialize Kalman Filter Variables % Initial guess %
% Given functions f(x,u) and h(x) x_hat = x0; P = P0; for k=1:N % Predict x_pred = f(x_hat, u(:,k)); F = jacobian_f(x_hat, u(:,k)); P_pred = F * P * F' + Q; P_pred = P + Q