end
% Implement the Kalman filter x_est = zeros(2, length(t)); P_est = zeros(2, 2, length(t)); x_est(:, 1) = x0; P_est(:, :, 1) = P0; for i = 2:length(t) % Prediction step x_pred = A * x_est(:, i-1); P_pred = A * P_est(:, :, i-1) * A' + Q;
Elias began typing into MATLAB, his fingers finding a rhythm:
Most “beginner” books drop the state equation on page 1:
Let’s walk through a MATLAB script that mirrors the style of Kim’s book. This is a 1D Kalman Filter for a slowly moving object.
end
% Implement the Kalman filter x_est = zeros(2, length(t)); P_est = zeros(2, 2, length(t)); x_est(:, 1) = x0; P_est(:, :, 1) = P0; for i = 2:length(t) % Prediction step x_pred = A * x_est(:, i-1); P_pred = A * P_est(:, :, i-1) * A' + Q; end % Implement the Kalman filter x_est =
Elias began typing into MATLAB, his fingers finding a rhythm: P_est = zeros(2
Most “beginner” books drop the state equation on page 1: 1) = x0
Let’s walk through a MATLAB script that mirrors the style of Kim’s book. This is a 1D Kalman Filter for a slowly moving object.