Method of Ship Wake Detection Based on Time-Frequency Analysis and Transfer Learning

Abstract

Ship wake detection is an effective method for undersea vehicles to detect and track surface ships. However, traditional wake detection methods based on time-domain features are limited by subjective experience and complex varying marine environments with certain limitations in the intelligent detection of ship wakes. Aiming at the problem of insufficient accuracy and efficiency of traditional wake detection methods in complex environments, this study introduces a deep learning theory to improve the independent learning and adaptive capabilities of the model. Subsequently, a novel ship wake detection method based on transfer learning and time-frequency analysis is proposed, considering the difficulty of obtaining ship wake samples. First, in this method, the time-frequency domain characteristics of the wake signal are extracted using short-time Fourier transform. Then, through the strategy of parameter freezing and fine-tuning, transfer learning based on a pre-trained convolutional neural network is completed. Finally, the effective detection of ship wakes using a small sample dataset is realized. Combining the wake detection experiment with actual measured data, the results show that, when compared with the traditional wake detection method, the correct rate of the wake detection method based on time-frequency analysis and transfer learning is increased by approximately 10%, with the highest reaching 97.49%. It combines the characteristics of a low time cost, low sample demand, and high recognition performance.

Keywords

• wake detection
• ship
• time-frequency analysis
• deep learning
• convolutional neural network
• transfer learning