Simulation of Multi-Target Tracking Using Fuzzy Adaptive Interacting Multiple Models

This study presents a simulation program for multi-target tracking utilizing a fuzzy adaptive interacting multiple model (FA-IMM) approach. The implementation integrates multiple sensor inputs for enhanced environmental perception to improve tracking accuracy. Key algorithmic components include fuzzy logic-based model transition probability adaptation and interactive model fusion mechanisms. For validation purposes, we conducted comparative analyses against traditional methods including standard Kalman filters and other model-based tracking algorithms. The simulation results demonstrate that our FA-IMM approach achieves superior performance in multi-target tracking scenarios, exhibiting enhanced robustness in complex environments through adaptive model switching and probability updates. This method shows significant potential for real-world applications requiring high-precision tracking, particularly in intelligent transportation systems and security surveillance domains. The core implementation involves MATLAB-based simulation frameworks with customizable model sets and fuzzy rule bases for dynamic adaptation.