Kalman Filtering Theory

Kalman filtering is a mathematical method used to estimate the true state of a system—like an object’s position or speed—by combining multiple noisy measurements over time. It predicts the current state based on previous data, then updates that prediction with new observations to improve accuracy. It accounts for uncertainties both in the measurements and in the system’s behavior, gradually honing in on the most probable true state. This technique is widely used in navigation, robotics, and tracking systems to provide reliable, real-time estimates even when data is imperfect.