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12 pages, 5358 KiB

Open AccessArticle

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

by Fang Ji, Guonan Li, Shaoqing Lu and Junshuai Ni

Appl. Sci. 2024, 14(4), 1341; https://doi.org/10.3390/app14041341 - 06 Feb 2024

Viewed by 458

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Underwater Acoustic Signal Processing)

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15 pages, 3478 KiB

Open AccessArticle

A Sound Velocity Profile Stratification Method Based on Maximum Density and Maximum Distance Clustering

by Jian Li, Yue Pan, Rong Li, Tianlong Zhu, Zhen Zhang, Mingyu Gu and Guangjie Han

Appl. Sci. 2024, 14(1), 182; https://doi.org/10.3390/app14010182 - 25 Dec 2023

Viewed by 521

Abstract

In the field of deep-sea positioning, this paper aims to enhance accuracy and computational efficiency in positioning calculations. We propose an improved method based on layered clustering of sound velocity profiles, where the profiles are stratified according to maximum distance and maximum density. [...] Read more.

In the field of deep-sea positioning, this paper aims to enhance accuracy and computational efficiency in positioning calculations. We propose an improved method based on layered clustering of sound velocity profiles, where the profiles are stratified according to maximum distance and maximum density. Subsequently, a secondary curve fitting is applied to the stratified data. Ultimately, the underwater positioning is conducted using the sound velocity profiles’ post-layered fitting. We compare our approach with traditional methods such as k-means clustering, layered clustering, and gradient-based stratification. Experimental results demonstrate that, in the application scenario of a USBL system with a transducer tilted at 30°, and under the premise of autonomously controlling the number of layers, our method significantly improves positioning accuracy. Full article

(This article belongs to the Special Issue Underwater Acoustic Signal Processing)

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12 pages, 13454 KiB

Open AccessArticle

A Low Frequency Noise Source Localization and Identification Method Based on a Virtual Open Spherical Vector Microphone Array

by Boquan Yang, Yuan Gao, Qiang Guo and Shengguo Shi

Appl. Sci. 2023, 13(7), 4368; https://doi.org/10.3390/app13074368 - 29 Mar 2023

Viewed by 1141

Abstract

Aiming at the problem of poor spatial resolution of low-frequency noise sources in a small-aperture spherical microphone array (SMA), this paper proposes a method for localizing and identifying low-frequency noise sources based on virtual-vector open SMA (‘p+v’ joint processing method of pressure and [...] Read more.

Aiming at the problem of poor spatial resolution of low-frequency noise sources in a small-aperture spherical microphone array (SMA), this paper proposes a method for localizing and identifying low-frequency noise sources based on virtual-vector open SMA (‘p+v’ joint processing method of pressure and velocity). Firstly, a virtual open SMA with a larger aperture is obtained using a virtual array extrapolation method. In this method, the virtual SMA and the actual SMA are regarded as a dual-radius SMA, and velocity information is obtained using finite difference elements of the same direction (azimuth and elevation) array of the virtual and actual SMA. At the same time, the sound pressure at the velocity position is obtained using the virtual SMA extrapolation method and the virtual vector array element SMA, whereby both velocity and sound pressure information is obtained. Finally, the vector signal processing technology is introduced into the generalized inverse beamforming algorithm (GIB). After determining the vector transfer function of the ‘p+v’ joint processing mode, a low-frequency-noise-source localization and identification method based on the vector signal processing GIB is proposed. The simulation and experiment results show that a virtual SMA with a large aperture can be obtained using a virtual array extrapolation method, and the GIB with sound pressure and velocity joint processing has a better spatial resolution. Full article

(This article belongs to the Special Issue Underwater Acoustic Signal Processing)

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16 pages, 2715 KiB

Open AccessArticle

Underwater Object Classification in SAS Images Based on a Deformable Residual Network and Transfer Learning

by Wenjing Gong, Jie Tian, Jiyuan Liu and Baoqi Li

Appl. Sci. 2023, 13(2), 899; https://doi.org/10.3390/app13020899 - 09 Jan 2023

Viewed by 1938

Abstract

To solve the problem of low classification accuracy caused by differences in object types, shapes, and scales in SAS images, an object classification method based on a deformable residual network and transfer learning is proposed. First, a lightweight deformable convolution module DSDCN was [...] Read more.

To solve the problem of low classification accuracy caused by differences in object types, shapes, and scales in SAS images, an object classification method based on a deformable residual network and transfer learning is proposed. First, a lightweight deformable convolution module DSDCN was designed by adding offsets to a traditional convolution, to adapt to objects with different shapes in SAS images, and the depthwise separable convolution was used to optimize the module. Second, a deformable residual network was designed with the DSDCN, which combined the traditional depth features with deformable features for object representation and improved the robustness of the model. Furthermore, the network was trained by the transfer learning method to save training time and prevent model overfitting. The model was trained and validated on the acquired SAS images. Compared with other existing state-of-the art models, the classification accuracy in this study improved by an average of 6.83% and had an advantage in the amount of computation, which is 108 M. On the deformation dataset, this method improved the accuracy, recall, and F1 scores by an average of 5.3%, 5.6%, and 5.8%, respectively. In the ablation experiments of the DSDCN module, the classification accuracy of the model with the addition of the DSDCN module improved by 5.18%. In addition, the training method of transfer learning also led to an improvement in model classification performance, reflected in the classification accuracy, which increased by 7.4%. Full article

(This article belongs to the Special Issue Underwater Acoustic Signal Processing)

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20 pages, 7369 KiB

Open AccessArticle

A Shallow Seafloor Reverberation Simulation Method Based on Generative Adversarial Networks

by Ning Hu, Xin Rao, Jiabao Zhao, Shengjie Wu, Maofa Wang, Yangzhen Wang, Baochun Qiu, Zhenjing Zhu, Zitong Chen and Tong Liu

Appl. Sci. 2023, 13(1), 595; https://doi.org/10.3390/app13010595 - 01 Jan 2023

Cited by 1 | Viewed by 1349

Abstract

Reverberation characteristics must be considered in the design of sonar. The research on reverberation characteristics is based on a large number of actual reverberation data. Due to the cost of trials, it is not easy to obtain actual lake and sea trial reverberation [...] Read more.

Reverberation characteristics must be considered in the design of sonar. The research on reverberation characteristics is based on a large number of actual reverberation data. Due to the cost of trials, it is not easy to obtain actual lake and sea trial reverberation data, which leads to a lack of actual reverberation data. Traditionally, reverberation data are obtained by modeling the generation mechanism of seafloor reverberation. The usability of the models requires a large amount of actual seafloor reverberation data to verify. In terms of the reverberation modeling theory, scattering models are mostly empirical, computationally intensive and inefficient. In order to solve the above obstacles, we propose a shallow seafloor reverberation data simulation method based on the generative adversarial network (GAN), which uses a small amount of actual reverberation data as reference samples to train the GAN to generate more reverberation data. The reverberation data generated by the GAN are compared with that simulated by traditional methods, and it is found that the reverberation data generated by the GAN meet the reverberation characteristics. Once the network is trained, the reverberation data are generated with very little computation. In addition, the method is universal and can be applied to any sea area. Compared with the traditional method, this method has a simple modeling idea, less computation and strong universality. It can be used as an alternative method for sea trials to provide data support for the study of seafloor reverberation characteristics, and it has broad application prospects in antireverberation technology research and active sonar design. Full article

(This article belongs to the Special Issue Underwater Acoustic Signal Processing)

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17 pages, 6222 KiB

Open AccessArticle

A Torpedo Target Recognition Method Based on the Correlation between Echo Broadening and Apparent Angle

by Zirui Wang, Jing Wu, Haitao Wang, Yukun Hao and Huiyuan Wang

Appl. Sci. 2022, 12(23), 12345; https://doi.org/10.3390/app122312345 - 02 Dec 2022

Cited by 2 | Viewed by 1527

Abstract

As acoustic decoys can simulate the scale of the target through orderly control of the echo delay, simulated acoustic decoys have scale characteristics similar to those of the scaled target. Consequently, simulated acoustic decoys make it difficult for active acoustic homing torpedoes to [...] Read more.

As acoustic decoys can simulate the scale of the target through orderly control of the echo delay, simulated acoustic decoys have scale characteristics similar to those of the scaled target. Consequently, simulated acoustic decoys make it difficult for active acoustic homing torpedoes to recognize acoustic decoys through traditional echo broadening or apparent angle. This will lead to a decrease in the anti-interference capability of torpedoes. In combat, acoustic decoys deceive torpedoes and deviate from the tracking course so that torpedoes cannot find the real target, or waste the range, eventually failing to strike the target and failing in combat. The accurate underwater target scale recognition of active acoustic homing torpedoes is considered a difficult technique. In this paper, we propose a target recognition method based on the correlation between target echo broadening and apparent angle. This specific simulation example shows that conventional target scale recognition methods cannot distinguish between suspended and homing acoustic decoys with virtual scale. By contrast, the target scale recognition method proposed in this paper can accurately distinguish between suspended and homing acoustic decoys with virtual scale at close range, under non-positive transverse port angle conditions. This method improves the anti-interference capability of torpedoes. In addition, it can improve the accuracy of active sonar recognition scale targets of ships, which guide active sonar target recognition. Full article

(This article belongs to the Special Issue Underwater Acoustic Signal Processing)

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18 pages, 2812 KiB

Open AccessArticle

Acoustic Shooting and Bounce Ray Method for Calculating Echoes of Complex Underwater Targets

by Gang Zhao, Wei Zheng, Naiwei Sun, Shen Shen and Xianyun Wu

Appl. Sci. 2022, 12(22), 11707; https://doi.org/10.3390/app122211707 - 17 Nov 2022

Cited by 1 | Viewed by 1115

Abstract

The acoustic scattering characteristics of a target are of great significance to the design of underwater acoustic detection systems. With the improvement in underwater weapon detection ability, the precision and accuracy of underwater target echo characteristic simulations are required to be higher and [...] Read more.

The acoustic scattering characteristics of a target are of great significance to the design of underwater acoustic detection systems. With the improvement in underwater weapon detection ability, the precision and accuracy of underwater target echo characteristic simulations are required to be higher and higher. The traditional underwater target simulation based on bright spot can no longer meet the needs of an underwater acoustic detection system for targeting fine feature recognition. This paper proposes a calculation using the fine complex underwater target echo bounce beam line method, in addition to the establishment of a bandwidth signal time domain calculation model and the integral dimension reduction to accelerate the algorithm on the basis of thorough target subspace division, combined with the geometrical acoustics method (GA) and physical acoustics (PA) for the pipeline space beam propagation tracking and aperture sound field to solve and realize the acoustic scattering characteristic calculation of a complex underwater target. Finally, the proposed method was verified by the Benchmark standard submarine. The accuracy and computational efficiency of the PA acceleration method are given, and the integrity of the target information calculated by the method is verified by acoustic ISAR imaging analysis. The simulation results show that the pipeline method of bouncing an acoustic beam is suitable for the simulation of complex underwater, target-wide band fine echoes. Full article

(This article belongs to the Special Issue Underwater Acoustic Signal Processing)

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12 pages, 576 KiB

Open AccessArticle

Fast Sparse Bayesian Learning-Based Channel Estimation for Underwater Acoustic OFDM Systems

by Yong-Ho Cho

Appl. Sci. 2022, 12(19), 10175; https://doi.org/10.3390/app121910175 - 10 Oct 2022

Cited by 2 | Viewed by 1199

Abstract

Harsh underwater channels and energy constraints are the two critical issues of underwater acoustic (UWA) communications. To achieve a high channel estimation performance under a severe underwater channel, sparse Bayesian learning (SBL)-based channel estimation was adopted for UWA orthogonal frequency division multiplexing (OFDM) [...] Read more.

Harsh underwater channels and energy constraints are the two critical issues of underwater acoustic (UWA) communications. To achieve a high channel estimation performance under a severe underwater channel, sparse Bayesian learning (SBL)-based channel estimation was adopted for UWA orthogonal frequency division multiplexing (OFDM) systems. Accurate channel estimation can guarantee the successful reception of transmitted data and reduce retransmission occurrences, thereby, leading to energy-efficient communications. However, SBL-based algorithms have improved performances in iterative ways, which require high power consumption. In this paper, a fast SBL algorithm based on a weighted learning rule for hyperparameters is proposed for channel estimation in a UWA-OFDM system. It was shown via numerical analysis that the proposed weighted learning rule enables fast convergence and more accurate channel estimation simultaneously. Simulation results confirm that the proposed algorithm achieves higher accuracy in channel estimation with much fewer iteration numbers in comparison to conventional SBL-based methods for a time-varying UWA channel. Full article

(This article belongs to the Special Issue Underwater Acoustic Signal Processing)

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16 pages, 3440 KiB

Open AccessArticle

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

by Junshuai Ni, Mei Zhao, Changqing Hu, Guotao Lv and Zheng Guo

Appl. Sci. 2022, 12(18), 9076; https://doi.org/10.3390/app12189076 - 09 Sep 2022

Cited by 2 | Viewed by 1089

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Underwater Acoustic Signal Processing)

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17 pages, 20672 KiB

Open AccessArticle

Automatic Tracking of Weak Acoustic Targets within Jamming Environment by Using Image Processing Methods

by Fan Yin, Chao Li, Haibin Wang and Fan Yang

Appl. Sci. 2022, 12(13), 6698; https://doi.org/10.3390/app12136698 - 01 Jul 2022

Cited by 3 | Viewed by 1050

Abstract

Bear time records, which are the accumulations of spatial spectrum estimates on the time axis, are often employed for passive sonar information processing. Multi-target jamming is a common difficulty in this approach due to the constraints of Rayleigh limit, and neither the conventional [...] Read more.

Bear time records, which are the accumulations of spatial spectrum estimates on the time axis, are often employed for passive sonar information processing. Multi-target jamming is a common difficulty in this approach due to the constraints of Rayleigh limit, and neither the conventional beamforming (CBF) nor minimum variance distortionless response (MVDR) technique can handle it well. This work presents a post-processing tracking framework based on visual pattern recognition algorithms to track weak acoustic targets within jamming environments, which includes target motion analysis, matched filtering, and principal component analysis-based denoising, and we call this ‘P-Gabor’ algorithm. The simulations and sea-trial experiments show that the proposed method can track a weak target successfully under −23 dB (signal-to-interference ratio) SIR, which is more effective than the references, especially in terms of using real-world data from sea trials. We further demonstrate that the method also has stable tracking performance at even −25 dB SNR (signal-to-noise ratio) circumstances. Full article

(This article belongs to the Special Issue Underwater Acoustic Signal Processing)

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8 pages, 915 KiB

Open AccessBrief Report

Passive Array-Invariant-Based Localization for a Small Horizontal Array Using Two-Dimensional Deconvolution

by Yujie Wang, Cheng Chi, Yu Li, Donghao Ju and Haining Huang

Appl. Sci. 2022, 12(18), 9356; https://doi.org/10.3390/app12189356 - 18 Sep 2022

Viewed by 1174

Abstract

Recently, the array-invariant method was proposed to passively localize sources of opportunity in shallow water. It exploits multiple arrivals which are different in terms of beam angle and travel time. Conventional plane-wave beamforming in the existing array-invariant method is used to obtain beam-time [...] Read more.

Recently, the array-invariant method was proposed to passively localize sources of opportunity in shallow water. It exploits multiple arrivals which are different in terms of beam angle and travel time. Conventional plane-wave beamforming in the existing array-invariant method is used to obtain beam-time migration. The resolution capability of conventional plane-wave beamforming is determined by array aperture, which, however, limits the localization accuracy of the existing array-invariant method. To improve the localization accuracy, this study proposes the use of two-dimensional (2D) deconvolution to obtain a better beam-time migration than in conventional plane-wave beamforming. Our simulation with a small horizontal array showed that the range estimation error of the proposed array-invariant method based on 2D deconvolution was only one-third of that of the existing method. The experiment also demonstrated the validity of our proposed method. Full article

(This article belongs to the Special Issue Underwater Acoustic Signal Processing)

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