针对无人船(Unmanned surface vehicle,USV)航行位姿观测数据的非高斯性/高斯性判别问题,提出一种基于主成分分析(Principal component analysis,PCA)和独立成分分析(Independent component analysis,ICA)模式融合的非高斯特征检测识别方法.首先,采用基于标准化加权平均和信息熵的数据预处理方法.其次,引入混合加权核函数并使用灰狼优化(Grey wolf optimization,GWO)算法进行参数优化,以提高PCA方法的准确性.同时,该算法采用一种新的非线性控制因子策略,提高全局和局部搜索能力.最后,建立了一种基于ICA和PCA联合的相关性分析方法来实现多维数据的降维,在降维数据的基础上综合T型多维偏度峰度检验法和KS(Kolmogorov-Smirnov)检验法进行非高斯性/高斯性特征检测识别.该方法考虑了非线性非高斯的噪声对降维结果精确度的影响,有效降低了多维数据非高斯检测的复杂度,同时也为后续在实际USV位姿估计等应用中提供了保障.实验表明,该方法具有较高的准确性和稳定性,可为USV航行位姿观测数据处理提供支持.
Abstract
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 discrimina-tion problem of unmanned surface vehicle(USV)navigation pose observation data.Firstly,a data preprocessing ap-proach 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 al-gorithm 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 dimen-sionality reduction results,which can effectively reduce the complexity of non-Gaussian detection of multidimen-sional data,and also provide guarantee for the subsequent applications such as actual USV attitude estimation.Ex-perimental results show high accuracy and stability,supporting the processing of USV navigation attitude observa-tion data.
关键词
主成分分析/混合核函数/灰狼优化算法/高维降维/非高斯
Key words
Principal component analysis(PCA)/mixed kernel function/grey wolf optimization(GWO)algorithm/high-dimensional dimensionality reduction/non-Gaussian
维普
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