Ship Shaft Frequency Extraction Based on Improved Stacked Sparse Denoising Auto-Encoder Network

Abstract

The modulation spectrum of ship radiated noise contains information on shaft frequency, which is an important feature used to identify ships and a key parameter involved in calculating the number of propeller blades. To improve the shaft frequency extraction accuracy, a ship shaft frequency extraction method based on an improved stacked sparse denoising auto-encoder network (SSDAE) is proposed. Firstly, the mathematical model of the ship radiated noise modulation spectrum is built and data simulation is carried out based on this model, combined with the actual ship parameters. Secondly, we trained the SSDAE model using the simulation data and made slight adjustments to this model by using both simulation and measured data to improve it. Finally, the experimental ship modulation spectrum information was input to the SSDAE model for denoising, enhancement, and regression estimation. Accordingly, the shaft frequency was extracted. The simulation and experimental results show that the shaft frequency extraction method based on the improved SSDAE model has high accuracy and good robustness, especially under the conditions of both missing line spectra and noise interference.