Sea Ice Type Identification Method Based on MEMS Sensor Network Combined With PCA-BPMIV-GRU

Sea ice poses a significant hazard to vessels navigating in high-latitude maritime regions. To effectively prevent the collision between ships and large sea ice (such as icebergs) and other similar accidents, it is crucial to identify sea ice and its types in a timely and accurate manner. However, traditional sea ice identification techniques, such as optical and radar remote sensing, are often limited by visibility conditions, suffering from poor accuracy, high measurement costs, and low identification efficiency. This article proposes a sea ice identification method based on a vibration sensor network, combined with the principal component analysis (PCA)-mean influence value optimization algorithm (BPMIV)-gated recurrent unit (GRU) strategy, which achieves low cost, high efficiency, and high accuracy in sea ice type detection. First, a cost-effective MEMS vibration sensor network is deployed to capture vibration information of sea ice. The data collected from six sensors at different locations at the same time are treated as independent samples to construct a dataset, which contains time-frequency domain characteristic data of three sea ice types: broken ice, flat ice, and open water. Second, the dimension reduction and feature optimization of vibration data were carried out by combining PCA and the improved BPMIV. This integration of unsupervised and supervised algorithms effectively eliminated unnecessary feature information, significantly improving model identification efficiency. Finally, seven commonly used classification algorithms are compared for the identification of three types of sea ice, evaluating their identification accuracy and efficiency under different dimensionality reduction methods. The results confirm that the GRU model achieves the best comprehensive performance. Experimental data show that the proposed method reduces the number of features to 9, resulting in a 24.2% decrease in parameters compared to the GRU model alone while achieving a high identification accuracy of 93.2%. The parameters and identification accuracy of the proposed method are superior to current sea ice identification technologies.