Kalman Filter Simulation for Aircraft Trajectory Tracking

In this documentation, we present a Kalman filter simulation program that models radar-based aircraft trajectory tracking. The Kalman filter is an optimal estimation algorithm designed to recursively estimate unknown system states, such as aircraft position and velocity, by combining noisy observations with dynamic system models. The simulation implements key components including state transition matrices, measurement models, and covariance propagation to demonstrate real-time filtering performance. Through this program, you can gain deep insights into Kalman filter mechanics, including how observational data and system models are integrated for state estimation. The modular code structure allows for customization of parameters like process noise covariance (Q) and measurement noise covariance (R) to adapt to different scenarios. Additionally, detailed documentation is provided to clarify implementation specifics such as the prediction-correction cycle, Jacobian calculations for nonlinear systems (if extended Kalman filter is implemented), and trajectory visualization methods. We hope this resource provides valuable information and practical assistance for understanding Kalman filter applications in target tracking systems.