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Remote Sensing
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Advances in Remote Sensing, Radar Techniques, and Their Applications
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Advances in Remote Sensing, Radar Techniques, and Their Applications

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

Deadline for manuscript submissions: 15 June 2024 | Viewed by 3432

Special Issue Editors

Prof. Dr. Nebojsa Doncov

E-Mail Website
Guest Editor
Department of telecommunications, Faculty of Electronic Engineering, University of Niš, 18000 Niš, Serbia
Interests: ground-penetrating radars; antennas; computational electromagnetics; artificial intelligence
Prof. Dr. Venceslav Kafedziski

E-Mail Website
Guest Editor
Institute of telecommunications, Faculty of Electrical Engineering and Information Technologies, Ss Cyril and Methodius University in Skopje, P.O. Box 574, Skopje, North Macedonia
Interests: radar signal processing; ground-penetrating radar; land mine detection; phased array and MIMO radar; microwave tomography; antennas and propagation; compressive sampling; machine learning; joint communication and sensing
Prof. Dr. Dusan Gleich

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, SI-2000 Maribor, Slovenia
Interests: synthetic aperture radar image enhancement; small-radar development; deep learning for SAR image enhancement; data interpretation, short-range radar development, radar signal processing, through the wall imaging, soil moisture estimation, and machine vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing and radar techniques and systems have experienced significant advancements in recent years, enabling a wide range of applications across various industries. Different types of radars, such as bistatic and multistatic radars, phased array and MIMO radars, have been developed. The main notable advances in remote sensing and radar techniques and applications are in the following areas: synthetic aperture radar (SAR) systems, radars mounted on unmanned aerial vehicles (UAVs), ground-penetrating radars (GPRs), automotive radars, radars for sensing in assisted living and motion recognition, OTH (over-the-horizon) radars, light detection and ranging (LiDAR) systems, etc. Advances in remote sensing techniques have expanded our ability to monitor and understand Earth's processes, leading to advancements in fields such as mapping, environmental conservation, disaster management and natural hazards study, resource exploration, agriculture, urban planning and national security. Continued research and development in these areas are expected to further enhance the capabilities and applications of remote sensing and radar systems in the future.

This Special Issue aims to report on the latest advances and trends in remote sensing and radar techniques and applications and the application of modern artificial intelligence, machine learning and big data methods for processing collected data and improving the performance of collected measurements. Papers of both a theoretical and applicative nature are welcomed.

Prof. Dr. Nebojsa Doncov
Prof. Dr. Venceslav Kafedziski
Prof. Dr. Dusan Gleich
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

  • SAR (synthetic aperture radar) and UAV mounted radars
  • GPR (ground-penetrating radar)
  • automotive radars
  • sensing in assisted living
  • OTH (over-the-horizon radar)
  • light detection and ranging (LiDAR)
  • phased array and MIMO radars
  • radar signal processing
  • AI (artificial intelligence), ML (machine learning) and big data
  • data fusion and analytics

Published Papers (5 papers)

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Research

25 pages, 13698 KiB  
Article
Joint Radar Jamming and Communication System Design Based on Universal Filtered Multicarrier Chirp Waveform
by Gaogao Liu, Ziyu Huang, Qidong Zhang, Beibei Mu and Hongfu Guo
Remote Sens. 2024, 16(8), 1383; https://doi.org/10.3390/rs16081383 - 14 Apr 2024
Viewed by 383
Abstract
In this article, we propose a joint waveform based on universal filtered multicarrier (UFMC) chirp for radar jamming and communication joint systems. Modulation of radar jamming chirp signals and communication signals on different subcarrier groups in the UFMC sub-band is used to achieve [...] Read more.
In this article, we propose a joint waveform based on universal filtered multicarrier (UFMC) chirp for radar jamming and communication joint systems. Modulation of radar jamming chirp signals and communication signals on different subcarrier groups in the UFMC sub-band is used to achieve the waveform design. The jamming signal in the waveform contains a frequency shift coefficient that depends on the delay time, which can effectively improve the anti-frequency hopping ability and enhance the overall jamming efficiency. Simultaneously jamming signals can provide assistance in channel estimation and equalization of communication, improving the information transmission quality of communication subsystems. We concluded through reasonable trade-off analysis that the combined weight of radar jamming and communication is closely related to the overall performance of the waveform. The simulation results show that the proposed UFMC chirp synthesized waveform has good jamming and communication performance. Software defined radio (SDR) simulation experiments demonstrated the effectiveness of this method in practical environments. Full article
(This article belongs to the Special Issue Advances in Remote Sensing, Radar Techniques, and Their Applications)
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21 pages, 2358 KiB  
Article
Three-Dimensional Human Pose Estimation from Micro-Doppler Signature Based on SISO UWB Radar
by Xiaolong Zhou, Tian Jin, Yongpeng Dai, Yongping Song and Kemeng Li
Remote Sens. 2024, 16(7), 1295; https://doi.org/10.3390/rs16071295 - 06 Apr 2024
Viewed by 523
Abstract
In this paper, we propose an innovative approach for transforming 2D human pose estimation into 3D models using Single Input–Single Output (SISO) Ultra-Wideband (UWB) radar technology. This method addresses the significant challenge of reconstructing 3D human poses from 1D radar signals, a task [...] Read more.
In this paper, we propose an innovative approach for transforming 2D human pose estimation into 3D models using Single Input–Single Output (SISO) Ultra-Wideband (UWB) radar technology. This method addresses the significant challenge of reconstructing 3D human poses from 1D radar signals, a task traditionally hindered by low spatial resolution and complex inverse problems. The difficulty is further exacerbated by the ambiguity in 3D pose reconstruction, as multiple 3D poses may correspond to similar 2D projections. Our solution, termed the Radar PoseLifter network, leverages the micro-Doppler signatures inherent in 1D radar echoes to effectively convert 2D pose information into 3D structures. The network is specifically designed to handle the long-range dependencies present in sequences of 2D poses. It employs a fully convolutional architecture, enhanced with a dilated temporal convolutions network, for efficient data processing. We rigorously evaluated the Radar PoseLifter network using the HPSUR dataset, which includes a diverse range of human movements. This dataset comprises data from five individuals with varying physical characteristics, performing a variety of actions. Our experimental results demonstrate the method’s robustness and accuracy in estimating complex human poses, highlighting its effectiveness. This research contributes significantly to the advancement of human motion capture using radar technology. It presents a viable solution for applications where precision and reliability in motion capture are paramount. The study not only enhances the understanding of 3D pose estimation from radar data but also opens new avenues for practical applications in various fields. Full article
(This article belongs to the Special Issue Advances in Remote Sensing, Radar Techniques, and Their Applications)
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22 pages, 1116 KiB  
Article
Radar-Jamming Decision-Making Based on Improved Q-Learning and FPGA Hardware Implementation
by Shujian Zheng, Chudi Zhang, Jun Hu and Shiyou Xu
Remote Sens. 2024, 16(7), 1190; https://doi.org/10.3390/rs16071190 - 28 Mar 2024
Viewed by 569
Abstract
In contemporary warfare, radar countermeasures have become multifunctional and intelligent, rendering the conventional jamming method and platform unsuitable for the modern radar countermeasures battlefield due to their limited efficiency. Reinforcement learning has been proven to be a practical solution for cognitive jamming decision-making [...] Read more.
In contemporary warfare, radar countermeasures have become multifunctional and intelligent, rendering the conventional jamming method and platform unsuitable for the modern radar countermeasures battlefield due to their limited efficiency. Reinforcement learning has been proven to be a practical solution for cognitive jamming decision-making in the cognitive electronic warfare. In this paper, we proposed a radar-jamming decision-making algorithm based on an improved Q-Learning algorithm. This improved Q-Learning algorithm ameliorated the problem of overestimating the Q-value that exists in the Q-Learning algorithm by introducing a second Q-table. At the same time, we performed a comprehensive design and implementation based on the classical Q-Learning algorithm, deploying it to a Field Programmable Gate Array (FPGA) hardware. We decomposed the implementation of the reinforcement learning algorithm into individual steps and described each step using a hardware description language. Then, the reinforcement learning algorithm can be computed on FPGA by linking the logic modules with valid signals. Experiments show that the proposed Q-Learning algorithm obtains considerable improvement in performance over the classical Q-Learning algorithm. Additionally, they confirm that the FPGA hardware can achieve great efficiency improvement on the radar-jamming decision-making algorithm implementation. Full article
(This article belongs to the Special Issue Advances in Remote Sensing, Radar Techniques, and Their Applications)
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20 pages, 6373 KiB  
Article
Radar High-Resolution Range Profile Rejection Based on Deep Multi-Modal Support Vector Data Description
by Yue Dong, Penghui Wang, Ming Fang, Yifan Guo, Lili Cao, Junkun Yan and Hongwei Liu
Remote Sens. 2024, 16(4), 649; https://doi.org/10.3390/rs16040649 - 09 Feb 2024
Viewed by 574
Abstract
Radar Automatic Target Recognition (RATR) based on high-resolution range profile (HRRP) has received intensive attention in recent years. In practice, RATR usually needs not only to recognize in-library samples but also to reject out-of-library samples. However, most rejection methods lack a specific and [...] Read more.
Radar Automatic Target Recognition (RATR) based on high-resolution range profile (HRRP) has received intensive attention in recent years. In practice, RATR usually needs not only to recognize in-library samples but also to reject out-of-library samples. However, most rejection methods lack a specific and accurate description of the underlying distribution of HRRP, which limits the effectiveness of the rejection task. Therefore, this paper proposes a novel rejection method for HRRP, named Deep Multi-modal Support Vector Data Description (DMMSVDD). On the one hand, it forms a more compact rejection boundary with the Gaussian mixture model in consideration of the high-dimensional and multi-modal structure of HRRP. On the other hand, it captures the global temporal information and channel-dependent information with a dual attention module to gain more discriminative structured features, which are optimized jointly with the rejection boundary. In addition, a semi-supervised extension is proposed to refine the boundary with available out-of-library samples. Experimental results based on measured data show that the proposed methods demonstrate significant improvement in the HRRP rejection performance. Full article
(This article belongs to the Special Issue Advances in Remote Sensing, Radar Techniques, and Their Applications)
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19 pages, 4474 KiB  
Article
Multi-Mission Oriented Joint Optimization of Task Assignment and Flight Path Planning for Heterogeneous UAV Cluster
by Xili Dong, Chenguang Shi, Wen Wen and Jianjiang Zhou
Remote Sens. 2023, 15(22), 5315; https://doi.org/10.3390/rs15225315 - 10 Nov 2023
Cited by 1 | Viewed by 826
Abstract
This paper puts forward a joint optimization algorithm of task assignment and flight path planning for a heterogeneous unmanned aerial vehicle (UAV) cluster in a multi-mission scenario (MMS). The basis of the proposed algorithm is to establish constraint and threat models of a [...] Read more.
This paper puts forward a joint optimization algorithm of task assignment and flight path planning for a heterogeneous unmanned aerial vehicle (UAV) cluster in a multi-mission scenario (MMS). The basis of the proposed algorithm is to establish constraint and threat models of a heterogeneous UAV cluster to simultaneously minimize range and maximize value gain and survival probability in an MMS under the constraints of task payload, range, and task requirement. On one hand, the objective function for the heterogeneous UAV cluster within an MMS is derived and it is adopted as a metric for assessing the performance of the joint optimization in task assignment and flight path planning. On the other hand, since the formulated joint optimization problem is a multi-objective, non-linear, and non-convex optimization model due to its multiple decision variables and constraints, the roulette wheel selection (RWS) principle and the elite strategy (ES) are introduced in an ant colony optimization (ACO) to solve the complex optimization model. The simulation results indicate that the proposed algorithm is superior and more efficient compared to other approaches. Full article
(This article belongs to the Special Issue Advances in Remote Sensing, Radar Techniques, and Their Applications)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Radar Jamming Decision-making Based on Improved Q-Learning and FPGA Hardware Implementation
Authors: Shujian Zheng; Chudi Zhang; Jun Hu; Shiyou Xu
Affiliation: Sun Yat-sen University
Abstract: In contemporary warfare, radar countermeasures have become multifunctional and intelligent, rendering the conventional jamming method and platform unsuitable for the modern radar countermeasures battlefield due to their limited efficiency. Reinforcement learning was proved to be a practical solution for cognitive jamming decision-making in the cognitive electronic warfare. In this paper, we proposed a radar jamming decision-making algorithm based on an improved Q-Learning algorithm.This improved Q-Learning algorithm ameliorated the problem of overestimating the Q-value that exists in the Q-Learning algorithm by introducing a second Q-table. At the same time, we performed a comprehensive design and implementation based on the classical Q-Learning algorithm, deploying it to a Field Programmable Gate Array (FPGA) hardware. We decomposed the implementation of the reinforcement learning algorithm into individual steps and described each step using hardware description language. Then the reinforcement learninf algorithm can be computed on FPGA by linking the logic modules with valid signals. Experiments show that the proposed Q-Learning algorithm obtain considerable improvement in performance over the classical Q-Learning algorithm. Additionally, they confirm that the FPGA hardware can achieve great efficiency improvement on the radar jamming decision-making algorithm implementation.

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