MATLAB Implementation of 3D Maneuvering Target Tracking Using Kalman Filter

This MATLAB source code implements a 3D maneuvering target tracking system using the Kalman filter algorithm. The Kalman filter is a widely-used recursive state estimation algorithm that optimally combines predictions and measurements to track dynamic systems. The implementation includes key components such as state transition matrices for 3D motion models, measurement update functions, and covariance propagation. The program features adaptive filtering capabilities to handle target maneuvers through process noise adaptation or multiple model approaches. The code structure demonstrates proper initialization of state vectors (position and velocity in 3D space), implementation of prediction and correction steps, and handling of measurement data from sensors. Comprehensive documentation accompanies the source code, including detailed usage instructions and sample datasets containing simulated target trajectories with various maneuver patterns. This implementation serves as an educational resource for understanding Kalman filter applications in target tracking and provides a foundation for further development of advanced tracking algorithms. Users can modify parameters such as sampling intervals, noise characteristics, and motion models to test different tracking scenarios.