Enhanced UAV Trajectory Tracking Using AIMM-IAKF with Adaptive Model Transition Probability

In complex Unmanned Aerial Vehicle (UAV) trajectory tracking scenarios, conventional Interacting Multiple Model (IMM) algorithms face challenges such as slow model switching rates and insufficient tracking accuracy. To address these limitations, this paper proposes an enhanced algorithm named Adaptive Interacting Multiple Model-Improved Adaptive Kalman Filter (AIMM-IAKF). The AIMM component dynamically adjusts the model transition probability matrix based on real-time model probability differences, overcoming the limitation of a fixed matrix in traditional IMM. Furthermore, the conventional Kalman filter is replaced with an Improved Adaptive Kalman Filter (IAKF), which introduces a convergence criterion and a suboptimal fading factor to optimize noise statistics. Simulation results demonstrate that, compared to the traditional IMM algorithm, the proposed AIMM-IAKF algorithm improves tracking accuracy by approximately 69%, achieves a faster model switching response, and exhibits superior stability with lower error fluctuation. The proposed framework provides a highly accurate and robust solution for tracking highly maneuvering UAVs.