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Space-Air-Ground-Ocean Integrated Sensing and Information Transmission

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

Deadline for manuscript submissions: 15 August 2024 | Viewed by 3953

Special Issue Editors


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Guest Editor
School of Ocean Engineering and Technology, Sun Yat-sen University, Zhuhai 519000, China
Interests: underwater acoustic communication and networks

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Guest Editor
National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China
Interests: underwater acoustic high-speed communication; underwater acoustic multi-user communication; underwater acoustic communication network
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Electronic Engineering, Tsinghua university, Beijing 100084, China
Interests: wireless multimedia communications; intelligent information processing

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Guest Editor
1. Department of Civil, Environmental, Land, Building Engineering and Chemistry—DICATECh, Polytechnic University of Bari, 70126 Bari, Italy
2. Geoinformatics Division, Department of Urban Planning & Environment, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden
Interests: change detection; SAR; photogrammetry; deep learning; land cover mapping; GEO big data; time series analysis; urban remote sensing; forest fire; mobile mapping
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Electrical and Computer Engineering, University of Minnesota, Athens, GA 30602, USA
Interests: machine learning; signal processing; data science; communications

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Guest Editor
College of Underwater Acoustic Engineering, Harbin Engeering University, Harbin, China
Interests: underwater acoustic communication technology; underwater acoustic target detection and location

Special Issue Information

Dear Colleagues,

Remote sensing refers to all non-contact long-distance detection technologies. Remote sensing technology utilizes modern means of transportation and sensors, and it obtains target characteristics from a long distance through technologies such as information transmission and fusion. At present, remote sensing technology has been widely applied in fields such as agriculture, forestry, geology, ocean engineering and science, meteorology, hydrology, military, and environmental protection. Single-source remote sensing technology is no longer sufficient to meet the needs of some application scenarios. Remote sensing technology based on multi-domain and multi-source information fusion is receiving increasing attention, and information sensing and transmission are the key technologies. The sensing and transmission of information in the air usually use radio and light waves as transmission media, but underwater, the transmission characteristics of radio and light waves are not suitable, so underwater information sensing and transmission mostly use acoustic waves as the medium. The purpose of this Special Issue is to collect the latest innovative research results in the field of space–air–ground–ocean-integrated sensing and information transmission, solve technical difficulties, and provide technical support for related fields. The scope of solicitation for this Special Issue includes, but is not limited to, the following research directions:

  • Deep-space and deep-sea sensing;
  • Underwater wireless communication and networks;
  • Underwater positioning;
  • Underwater remote sensing;
  • Polar and ocean exploration;
  • Geodesy and navigation;
  • Environmental remote sensing;
  • Forest and vegetation remote sensing;
  • Agriculture remote sensing;
  • Lidar and 3D visual perception;
  • Semantic representation and communications for multi-domain and multi-source information;
  • AI-enabled multi-domain and multi-source information transmission;
  • Quality and performance of integrated sensing and transmission;
  • Semantic-oriented compression and transmission;
  • Visual pattern recognition for multi-domain and multi-source information processing;
  • Theoretical aspects of integrated sensing and information transmission.

Dr. Jianmin Yang
Prof. Dr. Lu Ma
Dr. Yiping Duan
Dr. Andrea Nascetti
Dr. Qin Lu
Prof. Dr. Gang Qiao
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

  • deep-space and deep-sea sensing
  • underwater wireless communication and networks
  • underwater positioning
  • polar and ocean exploration
  • environmental remote sensing
  • forest and vegetation remote sensing
  • agriculture remote sensing
  • lidar and 3D visual perception

Published Papers (6 papers)

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Research

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25 pages, 18829 KiB  
Article
Enhanced Underwater Single Vector-Acoustic DOA Estimation via Linear Matched Stochastic Resonance Preprocessing
by Haitao Dong, Jian Suo, Zhigang Zhu, Haiyan Wang and Hongbing Ji
Remote Sens. 2024, 16(10), 1802; https://doi.org/10.3390/rs16101802 - 18 May 2024
Viewed by 413
Abstract
Underwater acoustic vector sensors (UAVSs) are increasingly utilized for remote passive sonar detection, but the accuracy of direction-of-arrival (DOA) estimation remains a challenging problem, particularly under low signal-to-noise ratio (SNR) conditions and complex background noise. In this paper, a comprehensive theoretical analysis is [...] Read more.
Underwater acoustic vector sensors (UAVSs) are increasingly utilized for remote passive sonar detection, but the accuracy of direction-of-arrival (DOA) estimation remains a challenging problem, particularly under low signal-to-noise ratio (SNR) conditions and complex background noise. In this paper, a comprehensive theoretical analysis is conducted on UAVS signal preprocessing subjected to gain-phase uncertainties for average acoustic intensity measurement (AAIM) and complex acoustic intensity measurement (CAIM)-based vector DOA estimation, aiming to explain the theoretical restrictions of intensity-based vector acoustic preprocessing approaches. On this basis, a generalized vector acoustic preprocessing optimization model is established in which the principle can be described as “maximizing the denoising performance under the constraints of an equivalent amplitude-gain response and phase-bias response”. A novel vector acoustic preprocessing method named linear matched stochastic resonance (LMSR) is proposed within the framework of matched stochastic resonance theory, which can naturally guarantee the linear gain-phase restrictions, as well achieving effective denoising performance. Numerical analyses demonstrate the superior vector DOA estimation performance of our proposed LMSR-AAIM and LMSR-CAIM methods in comparison to classical intensity-based AAIM and CAIM methods, especially under low-SNR conditions and non-Gaussian impulsive noise circumstances. Experimental verification conducted in the South China Sea further verifies its the effectiveness for practical application. This work can lay a solid foundation to break through the challenges of underwater remote vector acoustic DOA estimation under low-SNR conditions and complex ocean ambient noise and can provide important guidance for future research work. Full article
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20 pages, 855 KiB  
Article
Space–Air–Ground–Sea Integrated Network with Federated Learning
by Hao Zhao, Fei Ji, Yan Wang, Kexing Yao and Fangjiong Chen
Remote Sens. 2024, 16(9), 1640; https://doi.org/10.3390/rs16091640 - 4 May 2024
Viewed by 568
Abstract
A space–air–ground–sea integrated network (SAGSIN) is a promising heterogeneous network framework for the next generation mobile communications. Moreover, federated learning (FL), as a widely used distributed intelligence approach, can improve advanced network performance. In view of the combination and cooperation of SAGSINs and [...] Read more.
A space–air–ground–sea integrated network (SAGSIN) is a promising heterogeneous network framework for the next generation mobile communications. Moreover, federated learning (FL), as a widely used distributed intelligence approach, can improve advanced network performance. In view of the combination and cooperation of SAGSINs and FL, an FL-based SAGSIN framework faces a number of unprecedented challenges, not only from the communication aspect but also on the security and privacy side. Motivated by these observations, in this article, we first give a detailed state-of-the-art review of recent progress and ongoing research works on FL-based SAGSINs. Then, the challenges of FL-based SAGSINs are discussed. After that, for different service demands, basic applications are introduced with their benefits and functions. In addition, two case studies are proposed, in order to improve SAGSINs’ communication efficiency under a significant communication latency difference and to protect user-level privacy for SAGSIN participants, respectively. Simulation results show the effectiveness of the proposed algorithms. Moreover, future trends of FL-based SAGSINs are discussed. Full article
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16 pages, 2692 KiB  
Article
A Hierarchical Heuristic Architecture for Unmanned Aerial Vehicle Coverage Search with Optical Camera in Curve-Shape Area
by Lanjun Liu, Dechuan Wang, Jiabin Yu, Peng Yao, Chen Zhong and Dongfei Fu
Remote Sens. 2024, 16(9), 1502; https://doi.org/10.3390/rs16091502 - 24 Apr 2024
Viewed by 411
Abstract
This paper focuses on the problem of dynamic target search in a curve-shaped area by an unmanned aerial vehicle (UAV) with an optical camera. Our objective is to generate an optimal path for UAVs to obtain the maximum detection reward by a camera [...] Read more.
This paper focuses on the problem of dynamic target search in a curve-shaped area by an unmanned aerial vehicle (UAV) with an optical camera. Our objective is to generate an optimal path for UAVs to obtain the maximum detection reward by a camera in the shortest possible time, while satisfying the constraints of maneuverability and obstacle avoidance. First, based on prior qualitative information, the original target probability map for the curve-shaped area is modeled by Parzen windows with 1-dimensional Gaussian kernels, and then several high-value curve segments are extracted by density-based spatial clustering of applications with noise (DBSCAN). Then, given an example that a target floats down river at a speed conforming to beta distribution, the downstream boundary of each curve segment in the future time is expanded and predicted by the mean speed. The rolling self-organizing map (RSOM) neural network is utilized to determine the coverage sequence of curve segments dynamically. On this basis, the whole path of UAVs is a successive combination of the coverage paths and the transferring paths, which are planned by the Dubins method with modified guidance vector field (MGVF) for obstacle avoidance and communication connectivity. Finally, the good performance of our method is verified on a real river map through simulation. Compared with the full sweeping method, our method can improve the efficiency by approximately 31.5%. The feasibility is also verified through a real experiment, where our method can improve the efficiency by approximately 16.3%. Full article
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18 pages, 7846 KiB  
Article
A Deep Learning Localization Method for Acoustic Source via Improved Input Features and Network Structure
by Dajun Sun, Xiaoying Fu and Tingting Teng
Remote Sens. 2024, 16(8), 1391; https://doi.org/10.3390/rs16081391 - 14 Apr 2024
Viewed by 704
Abstract
Shallow water passive source localization is an essential problem in underwater detection and localization. Traditional matched-field processing (MFP) methods are sensitive to environment mismatches. Many neural network localization methods still have room for improvement in accuracy if they are further adjusted to underwater [...] Read more.
Shallow water passive source localization is an essential problem in underwater detection and localization. Traditional matched-field processing (MFP) methods are sensitive to environment mismatches. Many neural network localization methods still have room for improvement in accuracy if they are further adjusted to underwater acoustic characteristics. To address these problems, we propose a deep learning localization method via improved input features and network structure, which can effectively estimate the depth and the closest point of approach (CPA) range of the acoustic source. Firstly, we put forward a feature preprocessing scheme to enhance the localization accuracy and robustness. Secondly, we design a deep learning network structure to improve the localization accuracy further. Finally, we propose a method of visualizing the network to optimize the estimated localization results. Simulations show that the accuracy of the proposed method is better than other compared features and network structures, and the robustness is significantly better than that of the MFP methods. Experimental results further prove the effectiveness of the proposed method. Full article
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26 pages, 2940 KiB  
Article
Iterative Signal Detection and Channel Estimation with Superimposed Training Sequences for Underwater Acoustic Information Transmission in Time-Varying Environments
by Lin Li, Xiao Han and Wei Ge
Remote Sens. 2024, 16(7), 1209; https://doi.org/10.3390/rs16071209 - 29 Mar 2024
Viewed by 632
Abstract
Underwater signal processing is primarily based on sound waves because of the unique properties of water. However, the slow speed and limited bandwidth of sound introduce numerous challenges, including pronounced time-varying characteristics and significant multipath effects. This paper explores a channel estimation method [...] Read more.
Underwater signal processing is primarily based on sound waves because of the unique properties of water. However, the slow speed and limited bandwidth of sound introduce numerous challenges, including pronounced time-varying characteristics and significant multipath effects. This paper explores a channel estimation method utilizing superimposed training sequences. Compared with conventional schemes, this method offers higher spectral efficiency and better adaptability to time-varying channels owing to its temporal traversal. To ensure success in this scheme, it is crucial to obtain time-varying channel estimation and data detection at low SNRs given that superimposed training sequences consume power resources. To achieve this goal, we initially employ coarse channel estimation utilizing superimposed training sequences. Subsequently, we employ approximate message passing algorithms based on the estimated channels for data detection, followed by iterative channel estimation and equalization based on estimated symbols. We devise an approximate message passing channel estimation method grounded on a Gaussian mixture model and refine its hyperparameters through the expectation maximization algorithm. Then, we refine the channel information based on time correlation by employing an autoregressive hidden Markov model. Lastly, we perform numerical simulations of communication systems by utilizing a time-varying channel toolbox to simulate time-varying channels, and we validate the feasibility of the proposed communication system using experimental field data. Full article
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Review

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24 pages, 1717 KiB  
Review
Channel Prediction for Underwater Acoustic Communication: A Review and Performance Evaluation of Algorithms
by Haotian Liu, Lu Ma, Zhaohui Wang and Gang Qiao
Remote Sens. 2024, 16(9), 1546; https://doi.org/10.3390/rs16091546 - 26 Apr 2024
Viewed by 463
Abstract
Underwater acoustic (UWA) channel prediction technology, as an important topic in UWA communication, has played an important role in UWA adaptive communication network and underwater target perception. Although many significant advancements have been achieved in underwater acoustic channel prediction over the years, a [...] Read more.
Underwater acoustic (UWA) channel prediction technology, as an important topic in UWA communication, has played an important role in UWA adaptive communication network and underwater target perception. Although many significant advancements have been achieved in underwater acoustic channel prediction over the years, a comprehensive summary and introduction is still lacking. As the first comprehensive overview of UWA channel prediction, this paper introduces past works and algorithm implementation methods of channel prediction from the perspective of linear, kernel-based, and deep learning approaches. Importantly, based on available at-sea experiment datasets, this paper compares the performance of current primary UWA channel prediction algorithms under a unified system framework, providing researchers with a comprehensive and objective understanding of UWA channel prediction. Finally, it discusses the directions and challenges for future research. The survey finds that linear prediction algorithms are the most widely applied, and deep learning, as the most advanced type of algorithm, has moved this field into a new stage. The experimental results show that the linear algorithms have the lowest computational complexity, and when the training samples are sufficient, deep learning algorithms have the best prediction performance. Full article
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