Interactive Multiple Model Adaptive Filtering Algorithm for Maneuvering Target Tracking

In this document, we provide a detailed explanation of the Interactive Multiple Model (IMM) Adaptive Filtering Algorithm for maneuvering target tracking, along with its programming code implementation. The algorithm is designed to enhance tracking accuracy and stability when using single sensor measurements of azimuth and elevation angles. To achieve this objective, we employ multiple motion models to represent different target maneuvering patterns, utilizing interactive model switching and adaptive parameter adjustment mechanisms. The filter optimization process includes covariance matching and model probability updates to accommodate various environmental conditions and noise characteristics. In the programming code section, we demonstrate practical implementation techniques including: model set initialization using kinematic constraints, interactive mixing of model states through Markov transition probabilities, parallel Kalman filter execution for each model, and adaptive model probability calculation based on innovation sequences. The code features comprehensive comments and explanatory notes to facilitate better understanding and application of these advanced tracking techniques.