An Underwater Acoustic Target Recognition Method Based on Iterative Short-Time Fourier Transform

In the multifaceted marine environment, numerous factors affect the noise radiated by ships, thereby weakening the traditional spectral characteristics and diminishing the spectrum's ability to express identity information. Consequently, these changes result in reduced accuracy in recognition tasks. To address this issue, we propose a novel method for underwater acoustic target recognition aimed at extracting the intrinsic frequency distribution features of different frequency band energies in ship-radiated noise to supplement traditional time-frequency features, named ISNet. This method iteratively extracts the real and imaginary parts of the frequency features using short-time Fourier transform (STFT) and combines them with time-frequency features to form a feature matrix. After enhancement through fuzzy mixed features, the features are input into a dimensionality-reduced RSNet-18 network for training, and final predictions are obtained through a step-wise voting strategy. Experimental results show that this method surpasses existing approaches, achieving a recognition accuracy of 84.24% on the DeepShip dataset.