水下目标多模态深度学习分类识别研究

水下目标的分类识别对于水声探测具有重要意义。提出一种水下目标多模态深度学习分类识别方法。针对水声信号的一维时域模态和二维频域模态特征建立一种多模态特征融合的深度学习结构,结合长短时记忆网络和卷积神经网络的优点,对一维时域信号和二维频谱信号分别进行并行处理,对输出进行典型相关分析,形成特征融合表示,并利用相邻帧的相关性进行参数优化。利用实测水声信号对算法进行了验证。结果表明：提出的算法对于水下目标识别的精度有显著的提高。

Multimodal underwater target recognition method based on deep learning

Underwater target recognition has great significance for underwater acoustic detection. The multimodal underwater target recognition method was proposed based on deep learning. Due to the time domain features and frequency domain features, a multimodal structure was proposed to incorporate the long short-term memory neural network and convolution neural network. The time domain modal and frequency domain modal were processed respectively, the output of those networks was generated as feature fusion by canonical correlation analysis method. The temporal coherence of adjacent signal frame was utilized to improve the recognition accuracy. The experiments were implemented based on measured underwater acoustic signal. The results show that the proposed method improves the accuracy of underwater target recognition significantly.