% --- Correction Step (Measurement Update) --- z = measurements(k); K = P_pred / (P_pred + R); % Kalman Gain
% Storage for plotting estimates = zeros(num_samples,1); % --- Correction Step (Measurement Update) --- z
% Kalman Variables x_est = 0; % Initial guess (poor) P = 1; % Initial estimation error Q = 1e-5; % Process noise (we trust the model) R = noise_variance; % Measurement noise (we know sensor variance) % Kalman Variables x_est = 0
Why is this specific PDF so "hot"? And how can you use it to go from zero to hero in estimation theory? end % Plot the results figure
% Store result estimates(k) = x_est; end
% Plot the results figure; plot(measurements, 'r.', 'MarkerSize', 5); hold on; plot(estimates, 'b-', 'LineWidth', 2); legend('Noisy Measurements', 'Kalman Filter Estimate'); title('Phil Kim Method: Constant Voltage Estimation'); xlabel('Time (samples)'); ylabel('Voltage (V)'); grid on;