Kalman Filter Matlab ((better)) -

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

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); kalman filter matlab

After struggling with prediction/correction steps for a while, I implemented a basic Kalman filter for a 1D motion model in MATLAB. Sharing a clean working example. % Simulated measurements true_pos = 0:dt:10; meas =

Estimate position and velocity from noisy measurements. % Kalman loop for k = 1:length(meas) %

% Kalman loop for k = 1:length(meas) % Predict x = F x; P = F P*F' + Q;

est_pos(k) = x(1); end

Tuning Q and R is everything. Too low Q → filter ignores new data; too high → noisy output.