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Remote Sensing
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Intelligent Underwater Systems for Ocean Monitoring
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Intelligent Underwater Systems for Ocean Monitoring

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".

Deadline for manuscript submissions: closed (1 February 2022) | Viewed by 11646

Special Issue Editors

Dr. Yan Pailhas

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Guest Editor
Centre for Maritime Research and Experimentation (CMRE), Italy
Interests: ocean acoustics; signal processing; image processing
Prof. Dr. Francesco Maurelli

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Guest Editor
Marine Systems and Robotics, Jacobs University Bremen gGmbH, Campus Ring 1, D-28759 Bremen, Germany
Interests: marine robotics; ocean engineering; localization; fault management; knowledge representation; long-term autonomy
Special Issues, Collections and Topics in MDPI journals
Dr. Danilo Orlando

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Guest Editor
Department of Engineering, University of Niccolo Cusano, Via Don Carlo Gnocchi 3, 00166 Rome, Italy
Interests: the field of statistical signal processing with applications to radar and sonar
Special Issues, Collections and Topics in MDPI journals
Dr. Chengpeng Hao

E-Mail Website
Guest Editor
Chinese Academy of Sciences, Institute of Acoustics, Beijing, China
Interests: underwater robtics; remote sensing

Special Issue Information

Dear Colleagues,

Over the last two decades, The development of underwater systems for ocean monitoring has grown sharply. As a matter of fact, the demand of data collected by such systems has become more and more important for numerous research fields but also for industries including renewable energies, offshore oil and gas or underwater mining. It has become clear that a better understanding of our oceans is necessary for their preservation and safe exploitation. However, the ocean environment presents unprecedented challenges for data collection including, but not restricted to, poor precision in localization or poor communication rate. Therefore, the collection of relevant underwater data requires intelligence in the platforms, the data collection procedures and the sensors themselves on top of smart data processing.

This Special Issue calls for papers on intelligent underwater systems ranging from the Autonomous Underwater Vehicle design and autonomy to the development and the exploitation of underwater sensors as well as data. Contributions can also include new concepts for greater autonomy of system platforms, as, for instance, single and multi platform architectures and also smarter ways to gather and process data provided by a single sensor as well as sensor networks.

Dr. Yan Pailhas
Dr. Francesco Maurelli
Dr. Danilo Orlando
Dr. Chengpeng Hao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ocean remote sensing
  • ocean acoustics
  • signal processing
  • underwater acoustics
  • image processing

Published Papers (5 papers)

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Research

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27 pages, 1721 KiB  
Article
Reinforcement Learning Based Relay Selection for Underwater Acoustic Cooperative Networks
by Yuzhi Zhang, Yue Su, Xiaohong Shen, Anyi Wang, Bin Wang, Yang Liu and Weigang Bai
Remote Sens. 2022, 14(6), 1417; https://doi.org/10.3390/rs14061417 - 15 Mar 2022
Cited by 8 | Viewed by 2403
Abstract
In the complex and dynamically varying underwater acoustic (UWA) channel, cooperative communication can improve throughput for UWA sensor networks. In this paper, we design a reasonable relay selection strategy for efficient cooperation with reinforcement learning (RL), considering the characteristics of UWA channel variation [...] Read more.
In the complex and dynamically varying underwater acoustic (UWA) channel, cooperative communication can improve throughput for UWA sensor networks. In this paper, we design a reasonable relay selection strategy for efficient cooperation with reinforcement learning (RL), considering the characteristics of UWA channel variation and long transmission delay. The proposed scheme establishes effective state and reward expression to better reveal the relationship between RL and UWA environment. Meanwhile, simulated annealing (SA) algorithm is integrated with RL to improve the performance of relay selection, where exploration rate of RL is dynamically adapted by SA optimization through the temperature decline rate. Furthermore, the fast reinforcement learning (FRL) strategy with pre-training process is proposed for practical UWA network implementation. The whole proposed SA-FRL scheme has been evaluated by both simulation and experimental data. The simulation and experimental results show that the proposed relay selection scheme can converge more quickly than classical RL and random selection with the increase of the number of iterations. The reward, access delay and data rate of SA-FRL can converge at the highest value and are close to the ideal optimum value. All in all, the proposed SA-FRL relay selection scheme can improve the communication efficiency through the selection of the relay nodes with high link quality and low access delay. Full article
(This article belongs to the Special Issue Intelligent Underwater Systems for Ocean Monitoring)
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19 pages, 8548 KiB  
Article
Enhanced Pulsed-Source Localization with 3 Hydrophones: Uncertainty Estimates
by Despoina Pavlidi and Emmanuel K. Skarsoulis
Remote Sens. 2021, 13(9), 1817; https://doi.org/10.3390/rs13091817 - 07 May 2021
Cited by 1 | Viewed by 1956
Abstract
The uncertainty behavior of an enhanced three-dimensional (3D) localization scheme for pulsed sources based on relative travel times at a large-aperture three-hydrophone array is studied. The localization scheme is an extension of a two-hydrophone localization approach based on time differences between direct and [...] Read more.
The uncertainty behavior of an enhanced three-dimensional (3D) localization scheme for pulsed sources based on relative travel times at a large-aperture three-hydrophone array is studied. The localization scheme is an extension of a two-hydrophone localization approach based on time differences between direct and surface-reflected arrivals, an approach with significant advantages, but also drawbacks, such as left-right ambiguity, high range/depth uncertainties for broadside sources, and high bearing uncertainties for endfire sources. These drawbacks can be removed by adding a third hydrophone. The 3D localization problem is separated into two, a range/depth estimation problem, for which only the hydrophone depths are needed, and a bearing estimation problem, if the hydrophone geometry in the horizontal is known as well. The refraction of acoustic paths is taken into account using ray theory. The condition for existence of surface-reflected arrivals can be relaxed by considering arrivals with an upper turning point, allowing for localization at longer ranges. A Bayesian framework is adopted, allowing for the estimation of localization uncertainties. Uncertainty estimates are obtained through analytic predictions and simulations and they are compared against two-hydrophone localization uncertainties as well as against two-dimensional localization that is based on direct arrivals. Full article
(This article belongs to the Special Issue Intelligent Underwater Systems for Ocean Monitoring)
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16 pages, 18322 KiB  
Article
Feature Selection Based on Principal Component Regression for Underwater Source Localization by Deep Learning
by Xiaoyu Zhu, Hefeng Dong, Pierluigi Salvo Rossi and Martin Landrø
Remote Sens. 2021, 13(8), 1486; https://doi.org/10.3390/rs13081486 - 13 Apr 2021
Cited by 11 | Viewed by 2309
Abstract
Underwater source localization is an important task, especially for real-time operation. Recently, machine learning methods have been combined with supervised learning schemes. This opens new possibilities for underwater source localization. However, in many real scenarios, the number of labeled datasets is insufficient for [...] Read more.
Underwater source localization is an important task, especially for real-time operation. Recently, machine learning methods have been combined with supervised learning schemes. This opens new possibilities for underwater source localization. However, in many real scenarios, the number of labeled datasets is insufficient for purely supervised learning, and the training time of a deep neural network can be huge. To mitigate the problem related to the low number of labeled datasets available, we propose a two-step framework for underwater source localization based on the semi-supervised learning scheme. The first step utilizes a convolutional autoencoder to extract the latent features from the whole available dataset. The second step performs source localization via an encoder multi-layer perceptron trained on a limited labeled portion of the dataset. To reduce the training time, an interpretable feature selection (FS) method based on principal component regression is proposed, which can extract important features for underwater source localization by only introducing the source location without other prior information. The proposed approach is validated on the public dataset SWellEx-96 Event S5. The results show that the framework has appealing accuracy and robustness on the unseen data, especially when the number of data used to train gradually decreases. After FS, not only the training stage has a 95% acceleration but the performance of the framework becomes more robust on the receiver-depth selection and more accurate when the number of labeled data used to train is extremely limited. Full article
(This article belongs to the Special Issue Intelligent Underwater Systems for Ocean Monitoring)
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17 pages, 2956 KiB  
Technical Note
Robust and Fair Undersea Target Detection with Automated Underwater Vehicles for Biodiversity Data Collection
by Ranjith Dinakaran, Li Zhang, Chang-Tsun Li, Ahmed Bouridane and Richard Jiang
Remote Sens. 2022, 14(15), 3680; https://doi.org/10.3390/rs14153680 - 01 Aug 2022
Cited by 12 | Viewed by 2007
Abstract
Undersea/subsea data collection via automated underwater vehicles (AUVs) plays an important role for marine biodiversity research, while it is often much more challenging than the data collection above ground via satellites or AUVs. To enable the automated undersea/subsea data collection system, the AUVs [...] Read more.
Undersea/subsea data collection via automated underwater vehicles (AUVs) plays an important role for marine biodiversity research, while it is often much more challenging than the data collection above ground via satellites or AUVs. To enable the automated undersea/subsea data collection system, the AUVs are expected to be able to automatically track the objects of interest through what they can “see” from their mounted underwater cameras, where videos or images could be drastically blurred and degraded in underwater lighting conditions. To solve this challenge, in this work, we propose a cascaded framework by combining a DCGAN (deep convolutional generative adversarial network) with an object detector, i.e., single-shot detector (SSD), named DCGAN+SSD, for the detection of various underwater targets from the mounted camera of an automated underwater vehicle. In our framework, our assumption is that DCGAN can be leveraged to alleviate the impact of underwater conditions and provide the object detector with a better performance for automated AUVs. To optimize the hyperparameters of our models, we applied a particle swarm optimization (PSO)-based strategy to improve the performance of our proposed model. In our experiments, we successfully verified our assumption that the DCGAN+SSD architecture can help improve the object detection toward the undersea conditions and achieve apparently better detection rates over the original SSD detector. Further experiments showed that the PSO-based optimization of our models could further improve the model in object detection toward a more robust and fair performance, making our work a promising solution for tackling the challenges in AUVs. Full article
(This article belongs to the Special Issue Intelligent Underwater Systems for Ocean Monitoring)
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12 pages, 3977 KiB  
Technical Note
Sequential Parameter Estimation of Modal Dispersion Curves with an Adaptive Particle Filter in Shallow Water: Experimental Results
by Hong Liu, Kunde Yang and Qiulong Yang
Remote Sens. 2021, 13(12), 2387; https://doi.org/10.3390/rs13122387 - 18 Jun 2021
Cited by 1 | Viewed by 1566
Abstract
An adaptive particle filter method is presented for performing sequential geoacoustic estimation of a shallow water acoustic environment using the explosive sound sources. This approach treats environmental parameters and source–receiver distance as unknown random variables that evolve as the source moves. As a [...] Read more.
An adaptive particle filter method is presented for performing sequential geoacoustic estimation of a shallow water acoustic environment using the explosive sound sources. This approach treats environmental parameters and source–receiver distance as unknown random variables that evolve as the source moves. As a sequential estimation method, this approach reduces the expense of computation than genetic algorithm and yields results with the same accuracy. Comparing with standard Particle filter, proposed method can adjust control parameters to adapt to a rapidly changing environment. This approach is demonstrated on the shallow water sound propagation data which was collected during the ASIAEX 2001 experiment. The results indicate that the geoacoustic parameters are well estimated and source–receiver distance are also well determined. Full article
(This article belongs to the Special Issue Intelligent Underwater Systems for Ocean Monitoring)
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