Interactive Multiple Model Target Tracking Using UKF and EKF Filters

This article introduces Interactive Multiple Model (IMM) target tracking technology, featuring implementations using both Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) approaches. We provide complete code examples demonstrating key implementation aspects such as model transition probability management, filter interaction through mixing probabilities, and state estimation fusion. The code structure includes modules for handling multiple motion models (e.g., constant velocity, coordinated turn models) and demonstrates how to calculate model-conditioned estimates using both UKF's sigma point transformation and EKF's linearization techniques. Accompanying research papers are referenced to help readers gain deeper insight into implementation methodologies and application scenarios. Furthermore, we discuss the advantages of IMM's adaptive model switching capability, its limitations in computational complexity, and potential improvement directions such as optimized model sets or parallel processing implementations. Through this comprehensive overview, readers will develop a thorough understanding of IMM target tracking technology and obtain valuable references for future research projects.