Sparse Representation Classification Method for Active Sonar Target Based on Multi-order Fractional Fourier Domain Feature Fusion
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摘要: 海洋环境噪声和混响干扰严重、目标可分性差是主动声呐目标分类识别中的瓶颈问题。针对这一问题，该文基于主动声呐目标回波信号模型和分数阶傅里叶变换(FRFT)原理，推导了多阶次FRFT域特征表征形式，建立了FRFT域稀疏表示模型，提出了一种多阶次FRFT域特征融合的主动声呐目标稀疏表示分类方法。该方法通过FRFT的能量聚集性和稀疏分解的残差去除过程，达到了抑制噪声和混响干扰的目的；通过多阶次FRFT域特征的融合，增加目标之间的可分性，进而实现海洋环境中低信混比条件下的主动声呐目标分类。实验结果表明，所提方法在信混比达到0 dB的条件下，分类准确率能够达到90%以上。Abstract: Marine environment noise and reverberation interference are serious, and the poor target separability is the bottleneck problem in active sonar target classification and recognition. In order to solve this problem, based on the echo signal model of active sonar target and the principle of FRactional Fourier Transform (FRFT), this paper deduces the multi-order FRFT domain feature representation form, establishes the FRFT domain sparse representation model, and proposes a method to classify the sparse representation of active sonar targets with multi-order FRFT domain feature fusion. The method achieves the purpose of suppressing noise and reverberation interference through the energy aggregation of FRFT and removing the residual of sparse decomposition; Through the fusion of multi-order FRFT domain features, the separability of targets is improved, and the active sonar target classification with low SNR is realized. Experimental results show that the classification accuracy of the proposed method can reach more than 90% when the SNR is about 0 dB.
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