针对水下被动声呐目标分类识别问题，借签深度学习网络在图像、语音等领域的成功运用，提出一种基于深度自编码网络的舰船辐射噪声分类识别方法。首先使用Welch功率谱估计方法获得舰船辐射噪声的功率谱特征，然后对原始训练样本集结构优化得到新训练样本集，并构建训练深度自编码网络。依据总体正确识别概率和各类目标正确识别概率对网络参数进行优化设置，实现对舰船辐射噪声的分类识别。经过大量海上实录舰船辐射噪声的分类识别实验，验证了该方法的可行性和实用性。对比BP神经网络分类器，具有更高的正确分类识别概率。
In order to solve the problem of the classification and recognition of the targets in the underwater passive sonar, a method of the ship radiation noise based on the deep auto-encoding networks is proposed. The deep learning networks has been successfully applied in the fields of the image and speech. Firstly, Welch power spectrum estimation method are used to obtain the power spectral characteristics of the ship radiated noise. Secondly the original training sample set structure is optimized to obtain a new training sample set to construct and train a deep auto-encoding networks.Based on the correct recognition probability of all kinds of targets, the networks parameters are optimized to classify and identify the kind of ships. Using a large number of marine records, the ship radiated noises to experiment, the deep learning networks is a feasible and practicable method. Finally, it can have a higher correct classification recognition probability than the BP neural networks classifier.
2019,41(2): 124-130 收稿日期：2018-03-06
DOI：10.3404/j.issn.1672-7649.2019.02.025
分类号：TP18
基金项目：国家自然科学基金资助项目（61471378）
作者简介：严韶光(1993-),男,硕士研究生,主要从事水声目标识别研究
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