Abstract:Aiming at the problem of small-sample far-field low-frequency feature extraction and identification of large underwater vehicles, this paper reviews the traditional methods and intelligent methods of small-sample low-frequency feature extraction and identification at home and abroad from three aspects. The traditional methods of time-frequency domain alone, time-frequency domain combination and audiovisual sensory feature extraction require certain prior knowledge and assumptions, and are susceptible to environmental interference. Early intelligent methods such as expert systems, statistical methods and BP neural networks have problems such as poor portability, poor learning ability, low upper limit, and gradient vanishing. Deep intelligence methods such as deep confidence network (DBN), convolutional neural network (CNN), recurrent neural network (RNN), autoencoder (AE) neural network, generative adversarial network (GAN), and transfer learning deep network have low dependence on prior knowledge, can extract deep invariant features, are more stable than other methods, and have higher recognition accuracy, but also have the disadvantages of relying on the amount of data and insufficient interpretability. In the future, the deeper complementary integration of traditional methods and intelligent methods is expected to reduce the dependence of current methods on data volume and improve the interpretability of deep features.