1. What is a Kalman Filter? The Kalman filter is a recursive algorithm that estimates the state of a dynamic system from a series of incomplete and noisy measurements. It was developed by Rudolf E. Kálmán in 1960.
% Update K = P * H' / (H * P * H' + R); % Kalman gain x = x + K * (measurements(k) - H * x); P = (eye(2) - K * H) * P;
% Run Kalman filter estimated_positions = zeros(size(measurements)); for k = 1:length(measurements) % Predict x = A * x; P = A * P * A' + Q; kalman filter for beginners with matlab examples download
% Measurement noise (GPS error) R = 10;
% Simulate t = 0:dt:5; true_pos = 100 + 0 t + 0.5 (-9.8)*t.^2; measurements = true_pos + sqrt(R)*randn(size(t)); It was developed by Rudolf E
% Generate true motion and noisy measurements true_position = 0:dt:50; measurements = true_position + sqrt(R)*randn(size(true_position));
estimated_positions(k) = x(1); end
State = [position; velocity; acceleration]