Interactive Multiple Model Algorithm for Multi-Target Tracking and Programming Implementation

This paper presents an Interactive Multiple Model (IMM)-based multi-target tracking algorithm and its programming implementation. The algorithm utilizes sensor data to achieve target tracking, where a single sensor can simultaneously track multiple targets. Key implementation components include data preprocessing modules for filtering and normalizing sensor inputs, feature extraction functions that identify target characteristics, model training procedures for adapting to different motion patterns, and testing frameworks for performance validation. The core algorithm involves multiple model interaction through probability-weighted blending of Kalman filters, with state estimation refinement via model probability updates. We also discuss the algorithm's advantages in handling maneuvering targets and its limitations in computational complexity, proposing enhancement directions such as implementing deep learning models for improved pattern recognition and adaptive model selection. Overall, this IMM-based multi-target tracking algorithm demonstrates high practical value and has been widely applied across various domains including aerospace, autonomous vehicles, and surveillance systems.