A Recognition Method for Ship Motion Pattern Based on Nine-axis IMU

Motion pattern recognition is an important issue for achieving intelligent navigation of ships. To address the limitations of existing methods, including slow data update rates and strong environmental constraints, a ship motion pattern recognition method based on nine-axisinertial measurement unit (IMU) is proposed. The shortcomings of current ship motion sensing technologies are analyzed, and a nine-axis IMU consisting of an accelerometer, gyroscope, and magnetometer is utilized to identify ship motion parameters. To process long-duration continuous signals encompassing multiple motion patterns, a data segmentation algorithm based on hidden Markov model is developed. The expectation maximization algorithm is employed to estimate model parameters, enabling signal segmentation according to motion patterns and the extraction of single steady-state motion signals. The time-domain features that characterize ship motion patterns are then extracted from the segmented signals. To improve recognition accuracy, a support vector machine (SVM) algorithm based on a binomial tree structure is designed. The binary tree structure is constructed using the maximum cut problem, with SVM classifiers employed at decision nodes. The particle swarm algorithm is applied to optimize the model parameters. Experiments conducted using ship motion data collected from real ships validate the proposed method. Results show that the proposed recognition algorithm requires training only five SVM sub-classifiers for the recognition of six ship motion patterns, achieving an average recognition accuracy of 96.498%. Compared to traditional one-to-one and one-to-rest SVM multi-classification methods, the proposed method improves average recognition accuracy by 13.835% and 21.305%, respectively, while requiring fewer sub-classifiers for training. These findings demonstrate the superiority and efficiency of the proposed approach.