Editorial

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4 pages, 185 KiB

Open AccessEditorial

Recent Advances in Underwater Signal Processing

by Xuebo Zhang and Haixin Sun

Sensors 2023, 23(13), 5777; https://doi.org/10.3390/s23135777 - 21 Jun 2023

Viewed by 1464

Abstract

The ocean, covering 71% of the Earth’s surface, is integral to human life [...] Full article

(This article belongs to the Special Issue Recent Advances in Underwater Signal Processing)

Research

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15 pages, 1752 KiB

Open AccessArticle

INAM-Based Image-Adaptive 3D LUTs for Underwater Image Enhancement

by Xiao Xiao, Xingzhi Gao, Yilong Hui, Zhiling Jin and Hongyu Zhao

Sensors 2023, 23(4), 2169; https://doi.org/10.3390/s23042169 - 15 Feb 2023

Cited by 1 | Viewed by 1594

Abstract

To the best of our knowledge, applying adaptive three-dimensional lookup tables (3D LUTs) to underwater image enhancement is an unprecedented attempt. It can achieve excellent enhancement results compared to some other methods. However, in the image weight prediction process, the model uses the [...] Read more.

To the best of our knowledge, applying adaptive three-dimensional lookup tables (3D LUTs) to underwater image enhancement is an unprecedented attempt. It can achieve excellent enhancement results compared to some other methods. However, in the image weight prediction process, the model uses the normalization method of Instance Normalization, which will significantly reduce the standard deviation of the features, thus degrading the performance of the network. To address this issue, we propose an Instance Normalization Adaptive Modulator (INAM) that amplifies the pixel bias by adaptively predicting modulation factors and introduce the INAM into the learning image-adaptive 3D LUTs for underwater image enhancement. The bias amplification strategy in INAM makes the edge information in the features more distinguishable. Therefore, the adaptive 3D LUTs with INAM can substantially improve the performance on underwater image enhancement. Extensive experiments are undertaken to demonstrate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Recent Advances in Underwater Signal Processing)

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13 pages, 3425 KiB

Open AccessArticle

In Situ Underwater Localization of Magnetic Sensors Using Natural Computing Algorithms

by Roger Alimi, Elad Fisher and Kanna Nahir

Sensors 2023, 23(4), 1797; https://doi.org/10.3390/s23041797 - 05 Feb 2023

Cited by 4 | Viewed by 1423

Abstract

In the shallow water regime, several positioning methods for locating underwater magnetometers have been investigated. These studies are based on either computer simulations or downscaled laboratory experiments. The magnetic fields created at the sensors’ locations define an inverse problem in which the sensors’ [...] Read more.

In the shallow water regime, several positioning methods for locating underwater magnetometers have been investigated. These studies are based on either computer simulations or downscaled laboratory experiments. The magnetic fields created at the sensors’ locations define an inverse problem in which the sensors’ precise coordinates are the unknown variables. This work addresses the issue through (1) a full-scale experimental setup that provides a thorough scientific perspective as well as real-world system validation and (2) a passive ferromagnetic source with (3) an unknown magnetic vector. The latter increases the numeric solution’s complexity. Eight magnetometers are arranged according to a 2.5 × 2.5 m grid. Six meters above, a ferromagnetic object moves according to a well-defined path and velocity. The magnetic field recorded by the network is then analyzed by two natural computing algorithms: the genetic algorithm (GA) and particle swarm optimizer (PSO). Single- and multi-objective versions are run and compared. All the methods performed very well and were able to determine the location of the sensors within a relative error of 1 to 3%. The absolute error lies between 20 and 35 cm for the close and far sensors, respectively. The multi-objective versions performed better. Full article

(This article belongs to the Special Issue Recent Advances in Underwater Signal Processing)

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15 pages, 8499 KiB

Open AccessArticle

The Tacotron-Based Signal Synthesis Method for Active Sonar

by Yunsu Kim, Juho Kim, Jungpyo Hong and Jongwon Seok

Sensors 2023, 23(1), 28; https://doi.org/10.3390/s23010028 - 20 Dec 2022

Cited by 4 | Viewed by 1703

Abstract

The importance of active sonar is increasing due to the quieting of submarines and the increase in maritime traffic. However, the multipath propagation of sound waves and the low signal-to-noise ratio due to multiple clutter make it difficult to detect, track, and identify [...] Read more.

The importance of active sonar is increasing due to the quieting of submarines and the increase in maritime traffic. However, the multipath propagation of sound waves and the low signal-to-noise ratio due to multiple clutter make it difficult to detect, track, and identify underwater targets using active sonar. To solve this problem, machine learning and deep learning techniques that have recently been in the spotlight are being applied, but these techniques require a large amount of data. In order to supplement insufficient active sonar data, methods based on mathematical modeling are primarily utilized. However, mathematical modeling-based methods have limitations in accurately simulating complicated underwater phenomena. Therefore, an artificial intelligence-based sonar signal synthesis technique is proposed in this paper. The proposed method modified the major modules of the Tacotron model, which is widely used in the field of speech synthesis, in order to apply the Tacotron model to the field of sonar signal synthesis. To prove the validity of the proposed method, spectrograms of synthesized sonar signals are analyzed and the mean opinion score was measured. Through the evaluation, we confirmed that the proposed method can synthesize active sonar data similar to the trained one. Full article

(This article belongs to the Special Issue Recent Advances in Underwater Signal Processing)

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16 pages, 3502 KiB

Open AccessArticle

Track-before-Detect Algorithm for Underwater Diver Based on Knowledge-Aided Particle Filter

by Wenrong Yue, Feng Xu, Xiongwei Xiao and Juan Yang

Sensors 2022, 22(24), 9649; https://doi.org/10.3390/s22249649 - 09 Dec 2022

Cited by 2 | Viewed by 1131

Abstract

This work studies the underwater detection and tracking of diver targets under a low signal-to-reverberation ratio (SRR) in active sonar systems. In particular, a particle filter track-before-detect based on a knowledge-aided (KA-PF-TBD) algorithm is proposed. Specifically, the original echo data is directly used [...] Read more.

This work studies the underwater detection and tracking of diver targets under a low signal-to-reverberation ratio (SRR) in active sonar systems. In particular, a particle filter track-before-detect based on a knowledge-aided (KA-PF-TBD) algorithm is proposed. Specifically, the original echo data is directly used as the input of the algorithm, which avoids the information loss caused by threshold detection. Considering the prior motion knowledge of the underwater diver target, we established a multi-directional motion model as the state transition model. An efficient method for calculating the statistical characteristics of echo data about the extended target is proposed based on the non-parametric kernel density estimation theory. The multi-directional movement model set and the statistical characteristics of the echo data are used as the knowledge-aided information of the particle filter process: this is used to calculate the particle weight with the sub-area instead of the whole area, and then the particles with the highest weight are used to estimate the target state. Finally, the effectiveness of the proposed algorithm is proved by simulation and sea-level experimental data analysis through joint evaluation of detection and tracking performance. Full article

(This article belongs to the Special Issue Recent Advances in Underwater Signal Processing)

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17 pages, 10142 KiB

Open AccessArticle

HW/SW Platform for Measurement and Evaluation of Ultrasonic Underwater Communications

by Unai Fernández-Plazaola, Jesús López-Fernández, Eduardo Martos-Naya, José F. Paris and Francisco Javier Cañete

Sensors 2022, 22(17), 6514; https://doi.org/10.3390/s22176514 - 29 Aug 2022

Cited by 2 | Viewed by 1503

Abstract

The purpose of this work is to present a flexible system that supports the study of wideband underwater acoustic communications (UAC). It has been developed both to measure channels and to test transmission techniques under realistic conditions in the ultrasonic band. This platform [...] Read more.

The purpose of this work is to present a flexible system that supports the study of wideband underwater acoustic communications (UAC). It has been developed both to measure channels and to test transmission techniques under realistic conditions in the ultrasonic band. This platform consists of a hardware (HW) part that includes multiple hydrophones, projectors, analog front-ends, acquisition boards, and computers, and a software (SW) part for the generation, reception, and management of acoustic sounding signals and noise. UAC channels are among the most hostile ones and exhibit an important attenuation and distortion, essentially due to both multipath propagation, which results in a very long channel impulse response, and time-varying behavior, which produces a notable Doppler spread. To cope with this challenging medium, sophisticated transmission techniques must be employed. In this sense, adequate signal processing algorithms have been designed aiming not only at the analysis and characterization of underwater communication channels but also at the evaluation of diverse modulation, detection, and coding schemes, from Orthogonal Frequency Division Multiplexing (OFDM) to single-carrier digital modulations with a single-input multiple-output (SIMO) configuration that takes advantage of diversity techniques. Wideband sounding signals, to be injected into the sea from the transmitter side, are created with patterns that allow multiple tests on a batch. With offline processing of the captured data at the receiver side, different trials can be carried out in a very flexible manner. The different aspects of the platform are described in detail: the HW equipment used, the SW interface to control acquisition boards, and the signal processing algorithms to estimate the UAC channel response. The platform allows the analysis and design of new proposals for underwater communications systems that improve the performance of the current ones. Full article

(This article belongs to the Special Issue Recent Advances in Underwater Signal Processing)

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15 pages, 8821 KiB

Open AccessArticle

Autoencoder-Based Signal Modulation and Demodulation Methods for Sonobuoy Signal Transmission and Reception

by Jinuk Park, Jongwon Seok and Jungpyo Hong

Sensors 2022, 22(17), 6510; https://doi.org/10.3390/s22176510 - 29 Aug 2022

Cited by 2 | Viewed by 2680

Abstract

Sonobuoy is a disposable device that collects underwater acoustic information and is designed to transmit signals collected in a particular area to nearby aircraft or ships and sink to the seabed upon completion of its mission. In a conventional sonobuoy signal transmission and [...] Read more.

Sonobuoy is a disposable device that collects underwater acoustic information and is designed to transmit signals collected in a particular area to nearby aircraft or ships and sink to the seabed upon completion of its mission. In a conventional sonobuoy signal transmission and reception system, collected signals are modulated and transmitted using techniques such as frequency division modulation or Gaussian frequency shift keying. They are received and demodulated by an aircraft or a ship. However, this method has the disadvantage of a large amount of information being transmitted and low security due to relatively simple modulation and demodulation methods. Therefore, in this paper, we propose a method that uses an autoencoder to encode a transmission signal into a low-dimensional latent vector to transmit the latent vector to an aircraft or vessel. The method also uses an autoencoder to decode the received latent vector to improve signal security and to reduce the amount of transmission information by approximately a factor of a hundred compared to the conventional method. In addition, a denoising autoencoder, which reduces ambient noises in the reconstructed outputs while maintaining the merit of the proposed autoencoder, is also proposed. To evaluate the performance of the proposed autoencoders, we simulated a bistatic active and a passive sonobuoy environments. As a result of analyzing the sample spectrograms of the reconstructed outputs and mean square errors between original and reconstructed signals, we confirmed that the original signal could be restored from a low-dimensional latent vector by using the proposed autoencoder within approximately 4% errors. Furthermore, we verified that the proposed denoising autoencoder reduces ambient noise successfully by comparing spectrograms and by measuring the overall signal-to-noise ratio and the log-spectral distance of noisy input and reconstructed output signals. Full article

(This article belongs to the Special Issue Recent Advances in Underwater Signal Processing)

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17 pages, 6587 KiB

Open AccessArticle

Influence of Temporal and Spatial Fluctuations of the Shallow Sea Acoustic Field on Underwater Acoustic Communication

by Zhichao Lv, Libin Du, Huming Li, Lei Wang, Jixing Qin, Min Yang and Chao Ren

Sensors 2022, 22(15), 5795; https://doi.org/10.3390/s22155795 - 03 Aug 2022

Cited by 4 | Viewed by 1427

Abstract

In underwater acoustic communication (UAC) systems, the channel characteristics are mainly affected by spatiotemporal changes, which are specifically manifested by two factors: the effects of refraction and scattering caused by seawater layered media on the sound field and the random fluctuations from the [...] Read more.

In underwater acoustic communication (UAC) systems, the channel characteristics are mainly affected by spatiotemporal changes, which are specifically manifested by two factors: the effects of refraction and scattering caused by seawater layered media on the sound field and the random fluctuations from the sea floor and surface. Due to the time-varying and space-varying characteristics of a channel, the communication signals have significant variations in time and space. Furthermore, the signal shows frequency-selective fading in the frequency domain and signal waveform distortion in the time domain, which seriously affect the performance of a UAC system. Techniques such as error correction coding or space diversity are usually adopted by UAC systems to neutralize or eliminate the effects of deep fading and signal distortion, which results in a significant waste of limited communication resources. From the perspective of the sound field, this study used experimental data to analyze the spatiotemporal fluctuation characteristics of the signal and noise fields and then summarized the temporal and spatial variation rules. The influence of the system then guided the parameter configuration and network protocol optimization of the underwater acoustic communication system by reasonably selecting the communication signal parameters, such as frequency, bandwidth, equipment deployment depth, and horizontal distance. Full article

(This article belongs to the Special Issue Recent Advances in Underwater Signal Processing)

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15 pages, 4980 KiB

Open AccessArticle

Underwater Acoustic Signal Detection Using Calibrated Hidden Markov Model with Multiple Measurements

by Heewon You, Sung-Hoon Byun and Youngmin Choo

Sensors 2022, 22(14), 5088; https://doi.org/10.3390/s22145088 - 06 Jul 2022

Cited by 3 | Viewed by 1644

Abstract

It is important to find signals of interest (SOIs) when operating sonar systems. A threshold-based method is generally used for SOI detection. However, it induces a high false alarm rate at a low signal-to-noise ratio. On the other side, machine-learning-based detection is performed [...] Read more.

It is important to find signals of interest (SOIs) when operating sonar systems. A threshold-based method is generally used for SOI detection. However, it induces a high false alarm rate at a low signal-to-noise ratio. On the other side, machine-learning-based detection is performed to obtain more reliable detection results using abundant training data, costing intensive time and labor. We propose a method with favorable detection performance by using a hidden Markov model (HMM) for sequential acoustic data, which requires no separate training data. Since the detection results from HMM are significantly affected by the random initial parameters of HMM, the genetic algorithm (GA) is adopted to reduce the sensitivity of the initial parameters. The tuned initial parameters from GA are used as a start point for the subsequent Baum–Welch algorithm updating the HMM parameters. Furthermore, multiple measurements from arrays are exploited both in determining the proper initial parameters with GA and updating the parameters with the Baum–Welch algorithm. In contrast to the standard random selection of the initial point with single measurement, a stable initial point setting by the GA ensures improved SOI detections with the Baum–Welch algorithm using the multiple measurements, which are demonstrated in passive and active acoustic data. Particularly, the proposed method shows the most confidential detection in finding weak elastic surface waves from target, compared to existing methods such as conventional HMM. Full article

(This article belongs to the Special Issue Recent Advances in Underwater Signal Processing)

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22 pages, 29837 KiB

Open AccessArticle

The Design and Development of a Ship Trajectory Data Management and Analysis System Based on AIS

by Chengxu Feng, Bing Fu, Yasong Luo and Houpu Li

Sensors 2022, 22(1), 310; https://doi.org/10.3390/s22010310 - 31 Dec 2021

Cited by 8 | Viewed by 2325

Abstract

To address the data storage, management, analysis, and mining of ship targets, the object-oriented method was employed to design the overall structure and functional modules of a ship trajectory data management and analysis system (STDMAS). This paper elaborates the detailed design and technical [...] Read more.

To address the data storage, management, analysis, and mining of ship targets, the object-oriented method was employed to design the overall structure and functional modules of a ship trajectory data management and analysis system (STDMAS). This paper elaborates the detailed design and technical information of the system’s logical structure, module composition, physical deployment, and main functional modules such as database management, trajectory analysis, trajectory mining, and situation analysis. A ship identification method based on the motion features was put forward. With the method, ship trajectory was first partitioned into sub-trajectories in various behavioral patterns, and effective motion features were then extracted. Machine learning algorithms were utilized for training and testing to identify many types of ships. STDMAS implements such functions as database management, trajectory analysis, historical situation review, and ship identification and outlier detection based on trajectory classification. STDMAS can satisfy the practical needs for the data management, analysis, and mining of maritime targets because it is easy to apply, maintain, and expand. Full article

(This article belongs to the Special Issue Recent Advances in Underwater Signal Processing)

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24 pages, 10529 KiB

Open AccessArticle

Multi-Stage Feature Extraction and Classification for Ship-Radiated Noise

by Hamada Esmaiel, Dongri Xie, Zeyad A. H. Qasem, Haixin Sun, Jie Qi and Junfeng Wang

Sensors 2022, 22(1), 112; https://doi.org/10.3390/s22010112 - 24 Dec 2021

Cited by 15 | Viewed by 2587

Abstract

Due to the complexity and unique features of the hydroacoustic channel, ship-radiated noise (SRN) detected using a passive sonar tends mostly to distort. SRN feature extraction has been proposed to improve the detected passive sonar signal. Unfortunately, the current methods used in SRN [...] Read more.

Due to the complexity and unique features of the hydroacoustic channel, ship-radiated noise (SRN) detected using a passive sonar tends mostly to distort. SRN feature extraction has been proposed to improve the detected passive sonar signal. Unfortunately, the current methods used in SRN feature extraction have many shortcomings. Considering this, in this paper we propose a new multi-stage feature extraction approach to enhance the current SRN feature extractions based on enhanced variational mode decomposition (EVMD), weighted permutation entropy (WPE), local tangent space alignment (LTSA), and particle swarm optimization-based support vector machine (PSO-SVM). In the proposed method, first, we enhance the decomposition operation of the conventional VMD by decomposing the SRN signal into a finite group of intrinsic mode functions (IMFs) and then calculate the WPE of each IMF. Then, the high-dimensional features obtained are reduced to two-dimensional ones by using the LTSA method. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to realize the classification of different types of SRN sample. The simulation and experimental results demonstrate that the recognition rate of the proposed method overcomes the conventional SRN feature extraction methods, and it has a recognition rate of up to 96.6667%. Full article

(This article belongs to the Special Issue Recent Advances in Underwater Signal Processing)

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Review

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36 pages, 6289 KiB

Open AccessReview

Void Avoiding Opportunistic Routing Protocols for Underwater Wireless Sensor Networks: A Survey

by Rogaia Mhemed, William Phillips, Frank Comeau and Nauman Aslam

Sensors 2022, 22(23), 9525; https://doi.org/10.3390/s22239525 - 06 Dec 2022

Cited by 7 | Viewed by 1449

Abstract

One of the most challenging issues in the routing protocols for underwater wireless sensor networks (UWSNs) is the occurrence of void areas (communication void). That is, when void areas are present, the data packets could be trapped in a sensor node and cannot [...] Read more.

One of the most challenging issues in the routing protocols for underwater wireless sensor networks (UWSNs) is the occurrence of void areas (communication void). That is, when void areas are present, the data packets could be trapped in a sensor node and cannot be sent further to reach the sink(s) due to the features of the UWSNs environment and/or the configuration of the network itself. Opportunistic routing (OR) is an innovative prototype in routing for UWSNs. In routing protocols employing the OR technique, the most suitable sensor node according to the criteria adopted by the protocol rules will be elected as a next-hop forwarder node to forward the data packets first. This routing method takes advantage of the broadcast nature of wireless sensor networks. OR has made a noticeable improvement in the sensor networks’ performance in terms of efficiency, throughput, and reliability. Several routing protocols that utilize OR in UWSNs have been proposed to extend the lifetime of the network and maintain its connectivity by addressing void areas. In addition, a number of survey papers were presented in routing protocols with different points of approach. Our paper focuses on reviewing void avoiding OR protocols. In this paper, we briefly present the basic concept of OR and its building blocks. We also indicate the concept of the void area and list the reasons that could lead to its occurrence, as well as reviewing the state-of-the-art OR protocols proposed for this challenging area and presenting their strengths and weaknesses. Full article

(This article belongs to the Special Issue Recent Advances in Underwater Signal Processing)

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19 pages, 1482 KiB

Open AccessReview

Effects and Prospects of the Vibration Isolation Methods for an Atomic Interference Gravimeter

by Wenbin Gong, An Li, Chunfu Huang, Hao Che, Chengxu Feng and Fangjun Qin

Sensors 2022, 22(2), 583; https://doi.org/10.3390/s22020583 - 13 Jan 2022

Cited by 11 | Viewed by 2854

Abstract

An atomic interference gravimeter (AIG) is of great value in underwater aided navigation, but one of the constraints on its accuracy is vibration noise. For this reason, technology must be developed for its vibration isolation. Up to now, three methods have mainly been [...] Read more.

An atomic interference gravimeter (AIG) is of great value in underwater aided navigation, but one of the constraints on its accuracy is vibration noise. For this reason, technology must be developed for its vibration isolation. Up to now, three methods have mainly been employed to suppress the vibration noise of an AIG, including passive vibration isolation, active vibration isolation and vibration compensation. This paper presents a study on how vibration noise affects the measurement of an AIG, a review of the research findings regarding the reduction of its vibration, and the prospective development of vibration isolation technology for an AIG. Along with the development of small and movable AIGs, vibration isolation technology will be better adapted to the challenging environment and be strongly resistant to disturbance in the future. Full article

(This article belongs to the Special Issue Recent Advances in Underwater Signal Processing)

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