Widely-used algorithms (Kalman Filter)

The Kalman Filter is an algorithm that helps estimate the true state of a system — like a moving vehicle — by combining noisy measurements over time. It predicts where the object should be, then updates this estimate with actual sensor data, balancing trust between the prediction and the measurement. This process reduces errors caused by noise and uncertainty, providing more accurate and smooth tracking. The Kalman Filter is widely used in navigation, robotics, and signal processing where precise, real-time state estimation is essential.