在舰船移动网络中，数据特征的多维复杂属性对信息的提取和融合有较大影响。通过分析舰船移动网络通信数据的特点，将多特征信息融合方法结合小波算法和BP神经网络算法，并应用于舰船移动网络通信数据的特征提取和融合。对多个传感器获取的舰船移动网络通信数据进行预处理，得到多维复杂属性的综合信息，实现对舰船移动网络通信数据进行分类、筛选和预测。通过实验证明该方法能够有效降低复杂度，提高舰船移动网络通信数据处理的准确性和可靠性。
In ship mobile networks, the multi-dimensional complex attributes of data features have a great impact on information extraction and fusion. By analyzing the characteristics of ship mobile network communication data, the multi-feature information fusion method is combined with wavelet algorithm and BP neural network algorithm, and applied to the feature extraction and fusion of ship mobile network communication data. The ship mobile network communication data obtained by multiple sensors is preprocessed, and the comprehensive information of multi-dimensional complex attributes is obtained. The classification, screening and prediction of ship mobile network communication data are realized. The experimental results show that this method can effectively reduce the complexity and improve the accuracy and reliability of ship mobile network communication data processing.
2023,45(16): 153-156 收稿日期：2023-2-4
DOI：10.3404/j.issn.1672-7649.2023.16.032
分类号：TN919
作者简介：华创立(1979-),男,硕士,副教授,研究方向为网络工程技术
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