Non-Gaussian Feature Detection and Recognition Based on PCA and ICA Pattern Fusion

被引：0
|
作者：
Ge Q.-B. [1 ,2 ,3 ]
Cheng H.-R. [4 ]
Zhang M.-C. [4 ]
Zheng R.-J. [4 ]
Zhu J.-L. [4 ]
Wu Q.-T. [4 ]
机构：
[1] School of Automation, Nanjing University of Information Science & Technology, Nanjing
[2] Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing
[3] Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing
[4] School of Information Engineering, Henan University of Science and Technology, Luoyang
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2024年 / 50卷 / 01期
基金：
中国国家自然科学基金;
关键词：
grey wolf optimization (GWO) algorithm; high-dimensional dimensionality reduction; mixed kernel function; non-Gaussian; Principal component analysis (PCA);
D O I：
10.16383/j.aas.c230326
中图分类号：
学科分类号：
摘要：
A non-Gaussian feature detection and recognition method based on principal component analysis (PCA) and independent component analysis (ICA) pattern fusion is proposed for the non-Gaussian/Gaussian discrimination problem of unmanned surface vehicle (USV) navigation pose observation data. Firstly, a data preprocessing approach based on standardization weighted average and information entropy is adopted. Secondly, a mixed weighted kernel function is introduced and the grey wolf optimization (GWO) algorithm is used for parameter optimization to enhance the accuracy of the PCA method. Moreover, a new non-linear control factor strategy is applied in the algorithm to improve both global and local search abilities. Finally, a correlation analysis method based on ICA and PCA joint is established to realize the dimensionality reduction of multidimensional data, and the non-Gaussian/ Gaussian feature detection and recognition is carried out based on the comprehensive T-type multidimensional skewness kurtosis test and KS (Kolmogorov-Smirnov) test method on the basis of dimensionality reduction data. The proposed method takes into account the influence of nonlinear non-Gaussian noise on the accuracy of dimensionality reduction results, which can effectively reduce the complexity of non-Gaussian detection of multidimensional data, and also provide guarantee for the subsequent applications such as actual USV attitude estimation. Experimental results show high accuracy and stability, supporting the processing of USV navigation attitude observation data. © 2024 Science Press. All rights reserved.
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页码：169 / 180
页数：11
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