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Remote Sensing
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Targets Characterization by Radars
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Targets Characterization by Radars

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

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 13597

Special Issue Editors

Prof. Dr. Linrang Zhang

E-Mail Website
Guest Editor
National Key Lab of Radar Signal Processing, Xidian University, Xi’an 710071, China
Interests: radar signal processing; array signal processing; synthetic aperture radar
Prof. Dr. Hing Cheung So

E-Mail Website
Guest Editor
Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China
Interests: radar systems; adaptive beam-forming; tracking algorithms; systems engineering; remote imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Nan Liu

E-Mail Website
Guest Editor
National Key Lab of Radar Signal Processing, Xidian University, Xi’an 710071, China
Interests: synthetic aperture radar; array signal processing; radar detection

Special Issue Information

Dear Colleagues,

The radiation characteristic of radar targets is widely utilized in the applications of target classification/recognition, target detection algorithm design, target structure recovery, and radar system simulation. The target radiation characteristics, e.g., high resolution range profile (HRRP) for target classification/recognition and target radar cross section (RCS) statistics for detection algorithm design, can be extracted through multiple-aspect, multiple-polarization, and long-term radar observations. However, an increasing number of complicated targets have emerged in recent years, including the hypersonic vehicle and unmanned aerial vehicle (UAV) cluster. As a result, there is a need to study the radiation characteristics of these targets. Moreover, the radiation characteristic is not the only feature that can be extracted from the radar echoes. For example, the behavior characteristic of the UAV cluster can be obtained through long-term and high-resolution radar observations. Such behavior characteristics can reveal more in-depth information about the target, which is beneficial to enlarge the application area of radars. Therefore, research on the methods to extract new characteristics of target from radar echoes is required.

This Special Issue is devoted to highlighting the most advanced research in radar target characterization technology, methodology, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world and/or emerging problems will be welcome. The journal publishes original papers and invited review articles in all areas related to the target characterization of radar including, but not limited to, the following suggested topics:

  • Radar target scattering mechanism and characterization;
  • Radar target polarization characterization;
  • Target structure recovery through radar;
  • Target behavior characterization through radar;
  • Target characterization through high/low-resolution radar;
  • Hypersonic target characterization;
  • Cluster target characterization through radar;
  • Application of radar target characterization.

Prof. Dr. Linrang Zhang
Prof. Dr. Hing Cheung So
Dr. Nan Liu
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

  • radar target characterization
  • target scattering mechanism
  • target polarization characterization
  • target behavior characterization
  • hypersonic target
  • cluster target
  • target characterization application

Published Papers (6 papers)

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Research

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19 pages, 3986 KiB  
Article
Reconfigurable Intelligent Surface-Assisted Radar Deception Electronic Counter-Countermeasures
by Shanshan Zhao, Biao Xie, Ziwei Liu and Jirui An
Remote Sens. 2023, 15(21), 5149; https://doi.org/10.3390/rs15215149 - 27 Oct 2023
Viewed by 968
Abstract
A reconfigurable intelligent surface (RIS) is a promising technology for wireless communication and radar detection, owing to its superior ability to realize smart radio environments. Inspired by previous studies on RISs, this study deals with the use of RISs for radar electronic counter-countermeasures [...] Read more.
A reconfigurable intelligent surface (RIS) is a promising technology for wireless communication and radar detection, owing to its superior ability to realize smart radio environments. Inspired by previous studies on RISs, this study deals with the use of RISs for radar electronic counter-countermeasures (ECCMs) in deception jamming scenarios. At first, a RIS was applied to a monostatic radar, constructing a virtual multi-radar system combined with multi-beam receiving technology. Then, a data-fusion-based deception ECCM method for the proposed virtual multi-radar system was studied to discriminate the active false targets generated by deception jamming. A theoretical analysis of the target discrimination probability was derived. Because the location of RISs is the key to determining the target discrimination ability, the location optimization of the RIS was considered based on the theoretical analysis. Simulation results corroborate the deception ECCM ability of the proposed RIS-assisted virtual multi-radar system, enhancing the survivability of a radar system in a complex electromagnetic environment. Full article
(This article belongs to the Special Issue Targets Characterization by Radars)
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18 pages, 19844 KiB  
Article
Subset Selection Strategies Based on Target Positioning Characteristics for Anti-Jamming Technology
by Jieyi Liu, Maoguo Gong, Zhao Nie, Hao Li, Jingyao Liu and Shanshan Zhao
Remote Sens. 2022, 14(24), 6230; https://doi.org/10.3390/rs14246230 - 8 Dec 2022
Cited by 2 | Viewed by 1235
Abstract
For the discrimination of false targets, the discrimination probability can be improved by increasing the number of radar stations. However, that may result in a serious waste of equipment resources when too many radars are involved. An asymptotic subset selection strategy based on [...] Read more.
For the discrimination of false targets, the discrimination probability can be improved by increasing the number of radar stations. However, that may result in a serious waste of equipment resources when too many radars are involved. An asymptotic subset selection strategy based on target positioning characteristics is proposed to address the above issues. Several effective strategies are considered to select some transmitters and receivers to form a radar subset, such as the rapid shrinkage method, global shrinkage method, and predetermined size method, which can guarantee the preset discrimination performance of limited equipment resources and reduce the waste of resources. All of the selected stations have good spatial distribution or strong discrimination capacity in multistatic radar system. Compared with the exhaustive search, the proposed subset selection strategy affords a significant reduction in terms of time complexity. The simulation results show that the radar subset can maintain approximate discrimination performance with the original multistatic radar systems. At the same time, the proposed method optimizes the number of radar stations and reduces data processing time and required communication links, thus effectively saving operating costs. Full article
(This article belongs to the Special Issue Targets Characterization by Radars)
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23 pages, 7073 KiB  
Article
Intelligent Radar Jamming Recognition in Open Set Environment Based on Deep Learning Networks
by Yu Zhou, Song Shang, Xing Song, Shiyu Zhang, Tianqi You and Linrang Zhang
Remote Sens. 2022, 14(24), 6220; https://doi.org/10.3390/rs14246220 - 8 Dec 2022
Cited by 5 | Viewed by 1900
Abstract
Jamming recognition is an essential step in radar detection and anti-jamming in the complex electromagnetic environment. When radars detect an unknown type of jamming that does not occur in the training set, the existing radar jamming recognition algorithms fail to correctly recognize it. [...] Read more.
Jamming recognition is an essential step in radar detection and anti-jamming in the complex electromagnetic environment. When radars detect an unknown type of jamming that does not occur in the training set, the existing radar jamming recognition algorithms fail to correctly recognize it. However, these algorithms can only recognize this type of jamming as one that already exists in our jamming library. To address this issue, we present two models for radar jamming open set recognition (OSR) that can accurately classify known jamming and distinguish unknown jamming in the case of small samples. The OSR model based on the confidence score can distinguish known jamming from unknown jamming by assessing the reliability of the sample output probability distribution and setting thresholds. Meanwhile, the OSR model based on OpenMax can output the probability of jamming belonging to not only all known classes but also unknown classes. Experimental results show that the two OSR models exhibit high recognition accuracy for known and unknown jamming and play a vital role in sensing complex jamming environments. Full article
(This article belongs to the Special Issue Targets Characterization by Radars)
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19 pages, 5015 KiB  
Article
Classification of Radar Targets with Micro-Motion Based on RCS Sequences Encoding and Convolutional Neural Network
by Xuguang Xu, Cunqian Feng and Lixun Han
Remote Sens. 2022, 14(22), 5863; https://doi.org/10.3390/rs14225863 - 19 Nov 2022
Cited by 2 | Viewed by 1854
Abstract
Radar cross section (RCS) sequences, an easy-to-obtain target feature with small data volume, play a significant role in radar target classification. However, radar target classification based on RCS sequences has the shortcomings of limited information and low recognition accuracy. In order to overcome [...] Read more.
Radar cross section (RCS) sequences, an easy-to-obtain target feature with small data volume, play a significant role in radar target classification. However, radar target classification based on RCS sequences has the shortcomings of limited information and low recognition accuracy. In order to overcome the shortcomings of RCS-based methods, this paper proposes a spatial micro-motion target classification method based on RCS sequences encoding and convolutional neural network (CNN). First, we establish the micro-motion models of spatial targets, including precession, swing and rolling. Second, we introduce three approaches for encoding RCS sequences as images. These three types of images are Gramian angular field (GAF), Markov transition field (MTF) and recurrence plot (RP). Third, a multi-scale CNN is developed to classify those RCS feature maps. Finally, the experimental results demonstrate that RP is best at reflecting the characteristics of the target among those three encoding methods. Moreover, the proposed network outperforms other existing networks with the highest classification accuracy. Full article
(This article belongs to the Special Issue Targets Characterization by Radars)
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Review

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41 pages, 8369 KiB  
Review
Radar Target Characterization and Deep Learning in Radar Automatic Target Recognition: A Review
by Wen Jiang, Yanping Wang, Yang Li, Yun Lin and Wenjie Shen
Remote Sens. 2023, 15(15), 3742; https://doi.org/10.3390/rs15153742 - 27 Jul 2023
Cited by 2 | Viewed by 4702
Abstract
Radar automatic target recognition (RATR) technology is fundamental but complicated system engineering that combines sensor, target, environment, and signal processing technology, etc. It plays a significant role in improving the level and capabilities of military and civilian automation. Although RATR has been successfully [...] Read more.
Radar automatic target recognition (RATR) technology is fundamental but complicated system engineering that combines sensor, target, environment, and signal processing technology, etc. It plays a significant role in improving the level and capabilities of military and civilian automation. Although RATR has been successfully applied in some aspects, the complete theoretical system has not been established. At present, deep learning algorithms have received a lot of attention and have emerged as potential and feasible solutions in RATR. This paper mainly reviews related articles published between 2010 and 2022, which corresponds to the period when deep learning methods were introduced into RATR research. In this paper, the current research status of radar target characteristics is summarized, including motion, micro-motion, one-dimensional, and two-dimensional characteristics, etc. This paper reviews the progress of deep learning methods in the feature extraction and recognition of radar target characteristics in recent years, including space, air, ground, sea-surface targets, etc. Due to more and more attention and research results published in the past few years, it is hoped that this review can provide potential guidance for future research and application of deep learning in fields related to RATR. Full article
(This article belongs to the Special Issue Targets Characterization by Radars)
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Other

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20 pages, 8269 KiB  
Technical Note
Millimeter-Wave Radar Monitoring for Elder’s Fall Based on Multi-View Parameter Fusion Estimation and Recognition
by Xiang Feng, Zhengliang Shan, Zhanfeng Zhao, Zirui Xu, Tianpeng Zhang, Zihe Zhou, Bo Deng and Zirui Guan
Remote Sens. 2023, 15(8), 2101; https://doi.org/10.3390/rs15082101 - 16 Apr 2023
Cited by 2 | Viewed by 1594
Abstract
Human activity recognition plays a vital role in many applications, such as body falling surveillance and healthcare for elder’s in-home monitoring. Instead of using traditional micro-Doppler signals based on time-frequency distribution, we turn to another way and use the Relax algorithm to process [...] Read more.
Human activity recognition plays a vital role in many applications, such as body falling surveillance and healthcare for elder’s in-home monitoring. Instead of using traditional micro-Doppler signals based on time-frequency distribution, we turn to another way and use the Relax algorithm to process the radar echo so as to obtain the required parameters. In this paper, we aim at the multi-view idea in which two radars at different views work synchronously and fuse the features extracted from each radar, respectively. Furthermore, we discuss the common estimated time-frequency features and time-varying spatial features of multi-view radar-echo and then formulate the parameters matrix via principal component analysis, and finally transform them into the machine learning classifiers to make further comparisons. Simulations and results show that our proposed multi-view parameter fusion idea could lead to relative-high accuracy and robust recognition performance, which would provide a feasible application for future human–computer monitoring scenarios. Full article
(This article belongs to the Special Issue Targets Characterization by Radars)
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