Novel feature extraction of underwater targets by encoding hydro-acoustic signatures as image

Zaré, Mehdi; Nouri, Mahtab

doi:10.1016/j.apor.2023.103627

Cited by 2 publications

(1 citation statement)

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“…Traditional feature enhancement extraction techniques often need to rely on good feature engineering [1]. Zare et al [2] proposed a hydroacoustic image feature extraction method based on two-dimensional permutation entropy (PE) coding, and Li et al [3] proposed a feature extraction method with hierarchical entropy (HE). Zare et al [4] combined permutation entropy and slope entropy as features of intrinsic mode functions, which helped to improve the accuracy and stability of underwater signal recognition.…”

Section: Introductionmentioning

confidence: 99%

Research on a Feature Enhancement Extraction Method for Underwater Targets Based on Deep Autoencoder Networks

Ji,

Li,

et al. 2024

Applied Sciences

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The low-frequency line spectrum of the radiated noise signals of hydroacoustic targets contains features describing the intrinsic properties of the target that make the target susceptible to exposure. In order to extract the line spectral features of underwater acoustic targets, a method combining image processing and a deep autoencoder network (DAE) is proposed in this paper to enhance the low-frequency weak line spectrum of underwater targets in an extremely low signal-to-noise ratio environment based on the measured data of large underwater vehicles. A Gauss–Bernoulli restricted Boltzmann machine (G–BRBM) for real-value signal processing was designed and programmed by introducing a greedy algorithm. On this basis, the encoding and decoding mechanism of the DAE network was used to eliminate interference from environmental noise. The weak line spectrum features were effectively enhanced and extracted under an extremely low signal-to-noise ratio of 10–300 Hz, after which the reconstruction results of the line spectrum features were obtained. Data from large underwater vehicles detected by far-field sonar arrays were processed and the results show that the method proposed in this paper was able to adaptively enhance the line spectrum in a data-driven manner. The DAE method was able to achieve more than double the extractable line spectral density in the frequency band of 10–300 Hz. Compared with the traditional feature enhancement extraction method, the DAE method has certain advantages for the extraction of weak line spectra.

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Section: Introductionmentioning

confidence: 99%