Interactive Multiple Model Filter Design

This program implements an Interactive Multiple Model (IMM) filter design using the current statistical model, which achieves optimal tracking performance through sophisticated multi-model probability calculations. The IMM filter represents an advanced tracking technique that employs statistical data analysis to predict and monitor target movements. By integrating multiple kinematic models (such as constant velocity and maneuvering models), the algorithm can more accurately capture target trajectories through model probability updates and state mixing. The implementation typically involves key functions for model transition probability management, Kalman filter bank execution, and interactive state fusion. The design's primary advantage lies in its adaptability to diverse target types and environmental conditions, offering enhanced robustness and stability through Bayesian model probability weighting. Overall, this IMM filter design provides an effective and reliable solution suitable for various applications including aerospace tracking, autonomous navigation, and surveillance systems, with customizable model sets for specific operational scenarios.