A Bayesian Network-Enhanced IMM Algorithm for Complex Ship Trajectory Tracking

The tracking of the ship trajectory plays a critical role in the monitoring of maritime safety and navigation optimization. As a representative of multi-model target-tracking algorithms, the Interacting Multiple Model (IMM) algorithm achieves efficient trajectory tracking through the interaction of multiple filters, where the dynamic calculation of model weights is crucial. Based on the traditional IMM algorithm, this paper integrates Bayesian network technology to construct a dynamic model of ship motion. Through Bayesian inference, the IMM model weights are adaptively adjusted according to the probability distribution of the ship's motion attitude nodes, thereby enhancing the accuracy of state estimation. Experimental results demonstrate that the proposed method significantly enhances the adaptability and accuracy of the IMM algorithm in complex maritime environments, providing critical support for enhancing the reliability and robustness of ship trajectory tracking.