Filter Matlab Work: Kalman

% Simulated measurements true_pos = 0:dt:10; meas = true_pos + sqrt(R)*randn(size(true_pos));

% Plot plot(true_pos, 'g-', meas, 'ro', est_pos, 'b--') legend('True', 'Noisy', 'Kalman estimate') kalman filter matlab

dt = 0.1; % time step F = [1 dt; 0 1]; % state transition H = [1 0]; % measurement matrix Q = [0.01 0; 0 0.01]; % process noise R = 0.1; % measurement noise % Initial guess x = [0; 0]; P = eye(2); % Simulated measurements true_pos = 0:dt:10; meas =

Tuning Q and R is everything. Too low Q → filter ignores new data; too high → noisy output. % Simulated measurements true_pos = 0:dt:10

Happy filtering! 🔍

Estimate position and velocity from noisy measurements.