Interactive Multiple Model Particle Filter (IMMPF) Implementation and Simulations

This paper presents an implementation of the Interactive Multiple Model Particle Filter (IMMPF), a sophisticated filtering and tracking technique designed to handle multiple model systems with excellent uncertainty management capabilities. The algorithm combines particle filtering with multiple model interaction, where each particle represents a possible state hypothesis and model transition. We conducted comprehensive simulations of the IMMPF algorithm under three distinct outcome scenarios, implementing key components including model-conditioned particle propagation, model probability updates, and state estimation fusion. The simulation framework features importance sampling with systematic resampling, model transition probability matrices, and likelihood calculation modules. Our comparative analysis of the results provides deeper insights into the algorithm's behavior across different operating conditions, revealing its robustness in handling model uncertainty and state estimation challenges. These findings significantly enhance our understanding of IMMPF's potential applications in complex tracking systems and adaptive filtering scenarios.