The filter reads a new sensor measurement. It then calculates a weighted average of its own prediction and the sensor reading, giving more weight to whichever side is more trustworthy. Step-by-Step Mathematical Intuition
subplot(2,1,2); hold on; plot(0:dt:T, true_state(2,:), 'k--', 'LineWidth', 2); plot(0:dt:T, estimated_states(2,:), 'b-', 'LineWidth', 1.5); xlabel('Time (seconds)'); ylabel('Velocity (m/s)'); legend('True Velocity','KF Velocity Estimate','Location','best'); grid on; The filter reads a new sensor measurement
). Because no model is perfect, the uncertainty (variance) of our estimate increases during this step. 2. The Update Step (Measurement Update) Because no model is perfect, the uncertainty (variance)
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To find the true state of a system hidden behind this noise, engineers rely on the .
5. 2D Tracking Kalman Filter MATLAB Example (Position & Velocity)