Multi-Source Information Fusion: IMM Algorithm Simulation

This simulation program implements the Interacting Multiple Model (IMM) tracking algorithm as described in the "Multi-Source Information Fusion" textbook. The program simulates the IMM algorithm's core functionality, which primarily fuses information from multiple sensors and tracks target trajectories by establishing different motion models. The code implementation typically includes key modules such as model probability calculation, model interaction through mixing probabilities, and Kalman filter-based state estimation for each model. Through this simulation, users can better understand the principles and performance characteristics of the IMM algorithm. The program allows parameter adjustment for different scenarios, including model transition probabilities, noise covariance matrices, and initial state conditions, enabling algorithm optimization and performance analysis. The simulation provides valuable insights for researchers and developers working in multi-source information fusion, particularly in target tracking applications where maneuvering targets require multiple model hypotheses. The modular design facilitates experimentation with different model sets and fusion strategies, making it a significant tool for algorithm validation and educational purposes.