基于小波包分解和PCA-Attention-LSTM的舰船辐射噪声识别技术

为了改进舰船辐射噪声分类系统的性能，进一步提高识别准确率，文章提出了一种基于多特征的小波包分解在长短期记忆(LongShort-TermMemory,LSTM)网络中分类的方法。该方法首先通过小波包分解技术，分频段提取舰船辐射噪声的多种特征，将提取的特征利用主成分分析法(Principal Component Analysis, PCA)进行数据降维，通过添加注意力机制(Attention Mechanism)算法的LSTM网络，对辐射噪声结果分类，提高了学习效率和识别准确率。为了更精细地提取特征，分频段提取了舰船辐射噪声的时频域特征、小波变换特征和梅尔倒谱系数等特征，并将分频段与不分频段的特征、多特征与单一特征、不同信噪比间的算法性能进行对比。实验结果表明，基于小波包分解和PCA-Attention-LSTM的模型可以有效地提高舰船辐射噪声分类的性能，是一种可行的分类方法。

Ship radiated noise recognition technology based on wavelet packet decomposition and PCA-Attention-LSTM

In order to improve the performance of the ship radiated noise classification system and further improve the recognition accuracy, a method based on wavelet packet decomposition combined with multi-feature extraction in the long short-term memory (LSTM) network is proposed in this paper. This method first uses wavelet packet decomposition to extract multiple features of ship radiated noise in different frequency bands, and uses principal component analysis (PCA) for data reduction of the extracted features. By the LSTM network added with the attention mechanism algorithm the learning efficiency and recognition accuracy for radiated noise classification are improved. In order to extract the features precisely, the features in time-frequency domain and the features of wavelet transform and Mel-frequency cepstral coefficients (MFCC) of ship radiated noise are extracted in different frequency bands. Then, the performances of the algorithm for features with and without frequency band partition, multi-features and single feature, and different signal to noise ratios are compared. The experimental results show that the model based on wavelet packet decomposition and PCA-Attention-LSTM can effectively improve the performance of ship radiated noise classification and it is a feasible classification method.