An online outlier-robust extended Kalman filter via EM-algorithm for ship maneuvering data

This paper aims to enhance the accuracy and reliability of ship identification processes by preprocessing noise and outliers in the collected data. We propose a preprocessing method using the Expectation-Maximization (EM) algorithm and the Extended Kalman Filter (EKF) applied to ship trajectory and motion data, demonstrating significant advancements over existing methodologies. We introduce a novel application of one-dimensional Student's t-distributions with independent parameters by Q and R (measured noise covariance), enabling more precise modeling of sensor noise and outliers, thus improving data processing robustness. Our algorithm also incorporates adaptive learning capabilities, allowing adjustments to various environmental conditions, which enhances noise and outlier handling and broadens applicability across real-world scenarios. Experimental results indicate that our methods significantly improve data accuracy and ship identification reliability, underscoring their practical significance. These techniques provide robust technical support for accurate ship motion information and have considerable potential for maritime identification applications.