Performance Comparison of 3-Model and 2-Model Interacting Multiple Model Algorithms versus Standard Kalman Filter in Maneuvering Target Tracking

When tracking maneuvering targets, both 3-model and 2-model Interacting Multiple Model (IMM) algorithms alongside the standard Kalman Filter algorithm represent widely adopted approaches in current applications. The comparative analysis between these algorithms has emerged as a significant research focus. Through comprehensive study and analysis, we have determined that the IMM algorithm demonstrates superior tracking accuracy and precision compared to the standard Kalman Filter. The IMM algorithm's implementation typically involves multiple parallel Kalman filters with different motion models, combined with a Markov transition probability matrix that governs model switching. This architecture enables better adaptation to target maneuverability, resulting in enhanced performance during tracking operations. Consequently, when selecting algorithms for maneuvering target tracking applications, we recommend prioritizing the Interacting Multiple Model algorithm to improve both tracking effectiveness and accuracy. The IMM approach effectively handles model uncertainty by maintaining multiple filter hypotheses and computing the optimal combination through Bayesian probability calculations.