基于多核稀疏保持投影的多特征集典型相关分析的水下目标特征融合方法

Underwater Acoustic Target Feature Fusion Method Based on Multi-Kernel Sparsity Preserve Multi-Set Canonical Correlation Analysis

针对水下目标识别特征样本集高维小样本问题,提出了基于多核稀疏保持投影的多特征集典型相关分析的水下目标特征融合方法。该方法用多特征集典型相关分析算法对多域特征的整体相关程度进行定量分析,去除冗余和噪声特征,实现多域特征的融合,并利用多核稀疏保持投影算法,对提取的多域特征样本的稀疏重构性加以约束,增强了特征的判别能力。利用实测舰船辐射噪声数据验证基于核稀疏保持投影的多特征集典型相关分析的水下目标特征融合方法的有效性,与多特征集典型相关分析方法和核稀疏保持投影典型相关分析方法进行了对比,实验研究表明,提出的方法可以有效去除冗余和噪声特征,实现多域水下目标特征的融合,提高水下目标的识别正确率。

To solve high-dimensional and small-sample-size classification problem for underwater target recognition,a new feature fusion method is proposed based on multi-kernel sparsity preserve multi-set canonical correlation analysis. The multi-set canonical correlation analysis algorithm is used to quantitatively analyze the correlation of multidomain features,remove redundant and noise features,in order to achieve multi-domain feature fusion. The multikernel sparsely preserved projection algorithm is used to constrain the sparse reconstruction of the extracted multidomain feature samples,which enhances the feature’s classification ability. Results of applying real radiated noise datasets to underwater target recognition experiments show that our new method can effectively remove the redundancy and noise features, achieve the fusion of multi-domain underwater target features, and improve the recognition accuracy of underwater targets.