文章基本信息

标题：Extracting Features of Underwater Targets Using Kernel Fisher Discriminant Analysis
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作者：Chun-xue Shi ; Zhi-jing Zhou ; Hai-ming Zhao 等
期刊名称：IAENG International Journal of Computer Science
印刷版ISSN：1819-656X
电子版ISSN：1819-9224
出版年度：2020
卷号：47
期号：2
语种：English
出版社：IAENG - International Association of Engineers
摘要：This study proposes a method of feature extraction based on Kernel Fisher Discriminant Analysis (KFDA) to solve problems in the classification of underwater targets, specifically the large number of original characteristic parameters and significant nonlinearity. First, a large number of features are combined through serial feature fusion to establish a new feature vector space, and KFDA is used to extract the optimal nonlinear discriminant features. Second, a test bed for an underwater experiment featuring a data processing system, echo signal acquisition, and feature extraction is described. Finally, underwater acoustic experiments are carried out, and the results of the measurement data indicate that the proposed method is superior to currently used techniques in the area.
关键词：Kernel Fisher Discriminant Analysis(KFDA);Feature Extraction;Feature Fusion;Underwater Targets;Classification