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

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 i...

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Vydané v:Applied sciences Ročník 14; číslo 4; s. 1341
Hlavní autori: Ji, Fang, Li, Guonan, Lu, Shaoqing, Ni, Junshuai
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.02.2024
Predmet:
Acoustics
Algorithms
Analysis
Convex analysis
deep autoencoder network
Entropy
Image processing
large underwater vehicles
line spectrum enhancement
Methods
Neural networks
Optimization
Signal processing
Signal to noise ratio
Sonar systems
very low signal-to-noise ratio
ISSN:2076-3417, 2076-3417
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:2076-3417
2076-3417
DOI:10.3390/app14041341