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Advanced Technology of Target Detection, Tracking, Imaging, and Recognition for...
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Advanced Technology of Target Detection, Tracking, Imaging, and Recognition for Radar

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 7143

Special Issue Editors

Dr. Jia Su

E-Mail Website
Guest Editor
School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
Interests: interference mitigation; clutter suppression; weak target detection; muti-dimensional signal processing
Dr. Yifei Fan

E-Mail Website
Guest Editor
School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
Interests: radar; sea clutter modelling; sea clutter property analysis; clutter suppression; weak target detection; artificial intelligence
Dr. Dong Li

E-Mail Website
Guest Editor
School of Microelectronics and Communication Engineering, Chongqing University, No.174 Shazhengjie, Shapingba District, Chongqing 400044, China
Interests: SAR/ISAR imaging; moving target detection and tracking; parameter estimation

Special Issue Information

Dear Colleagues,

Radar has been widely applied in civil and defense applications. However, modern radar systems undergo significant performance degradation in strong clutter and interference environments. Meanwhile, the emergence of novel unmanned targets also poses great challenges to target detection, tracking, imaging, and recognition.

With the improvement of radar components, data transmission rates, and computational processing capabilities, the degree of freedom for radar systems is increasing. It is of great significance to exploit the multi-dimensional characteristics of radar systems in space, time, frequency, energy, polarization, etc., to improve radar performance under complex environments.

This Special Issue covers advanced techniques for target detection, tracking, imaging, and recognition from different levels, ranging from mission levels (swarms or distributed sensors) to a signal processing perspective (including, but not limited to, multi-dimensional modeling and analysis, adaptive beamforming,  weak signal extraction, artificial intelligence, etc.), as well as hardware design, encompassing ground-based, ship-based, airborne and spaceborne radar sensors.

This Special Issue will summarize the latest research efforts ongoing in this field, which enable significant improvements in radar operational performance in challenging clutter and interference scenarios.

Dr. Jia Su
Dr. Yifei Fan
Dr. Dong Li
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. Electronics 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 2400 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 detection
  • tracking
  • SAR/ISAR imaging
  • recognition
  • interference mitigation
  • clutter suppression
  • artificial intelligence
  • multi-dimensional signal processing

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Published Papers (4 papers)

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Research

14 pages, 4347 KiB  
Article
Domain Adaptive Few-Shot Learning for ISAR Aircraft Recognition with Transferred Attention and Weighting Importance
by Binquan Li, Yuan Yao and Qiao Wang
Electronics 2023, 12(13), 2909; https://doi.org/10.3390/electronics12132909 - 2 Jul 2023
Cited by 1 | Viewed by 1577
Abstract
With the enhancement of air-based and space-based perception capabilities, space-aeronautics incorporation and integration is growing in importance. Full domain awareness is crucial for integrated perception systems, in which domain adaptation is one of the key problems in improving the performance of cross-domain perception. [...] Read more.
With the enhancement of air-based and space-based perception capabilities, space-aeronautics incorporation and integration is growing in importance. Full domain awareness is crucial for integrated perception systems, in which domain adaptation is one of the key problems in improving the performance of cross-domain perception. Deep learning is currently an advanced technique for complex inverse synthetic aperture radar (ISAR) object recognition. However, the training procedure needs many annotated samples, which is insufficient for certain targets, such as aircraft. Few-shot learning provides a new approach to solving the above problem by transferring useful knowledge from other domains, such as optical images from satellites. Nevertheless, it fails to fully consider the domain shift between the source and target domains, generally neglecting the transferability of training samples in the learning process. Consequently, it produces suboptimal recognition accuracy. To address the composite problems mentioned above, we propose a domain adaptive few-shot learning method from satellites to an ISAR called S2I-DAFSL for aircraft recognition tasks. Furthermore, unlike conventional domain adaptation methods that directly align the distributions, the attention transferred importance-weighting network (ATIN) is proposed to improve the transferability in the domain adaptation procedure. Compared with state-of-the-art methods, it shows that the proposed method achieves better performance, increasing the accuracy and effectiveness of classification, which is more suitable for cross-domain few-shot ISAR aircraft recognition tasks. Full article
(This article belongs to the Special Issue Advanced Technology of Target Detection, Tracking, Imaging, and Recognition for Radar)
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16 pages, 7073 KiB  
Article
A Novel Coherent Integration Algorithm for Maneuvering Target Detection Based on Symmetric Instantaneous Autocorrelation Function
by Yunpeng Mi, Yunhua Zhang and Jiefang Yang
Electronics 2023, 12(11), 2363; https://doi.org/10.3390/electronics12112363 - 23 May 2023
Viewed by 1296
Abstract
Detection and parameter estimation of maneuvering targets having a jerking motion are some of the challenging problems for modern radar systems. Such targets usually introduce range migration (RM) and Doppler frequency migration (DFM) problems leading to serious performance degradation in detection. To address [...] Read more.
Detection and parameter estimation of maneuvering targets having a jerking motion are some of the challenging problems for modern radar systems. Such targets usually introduce range migration (RM) and Doppler frequency migration (DFM) problems leading to serious performance degradation in detection. To address these problems, a novel coherent integration (CI) algorithm is proposed based on a new symmetric instantaneous autocorrelation function (NSIAF), which can be utilized to reduce the order on the slow time and to eliminate the linear range migration (LRM) first. Then, the jerk and acceleration of the target are estimated after applying the keystone transform (KT) and the scaled Fourier transform (SFT); both of these are then used to construct the reference function for matched filtering. Finally, CI and target detection can be accomplished by the scaled inverse Fourier transform (SCIFT) after matched filtering. Both simulation data (this work) and practical radar experiment data (data set of others) were processed to validate the proposed algorithm. Compared with other representative algorithms, our algorithm can achieve a good balance between computational complexity and detection performance. Full article
(This article belongs to the Special Issue Advanced Technology of Target Detection, Tracking, Imaging, and Recognition for Radar)
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18 pages, 4514 KiB  
Article
Vision-Based Support for the Detection and Recognition of Drones with Small Radar Cross Sections
by Safa E. Abdelsamad, Mohammed A. Abdelteef, Othman Y. Elsheikh, Yomna A. Ali, Tarik Elsonni, Maha Abdelhaq, Raed Alsaqour and Rashid A. Saeed
Electronics 2023, 12(10), 2235; https://doi.org/10.3390/electronics12102235 - 15 May 2023
Cited by 8 | Viewed by 2198
Abstract
Drones are increasingly vital in numerous fields, such as commerce, delivery services, and military operations. Therefore, it is essential to develop advanced systems for detecting and recognizing drones to ensure the safety and security of airspace. This paper aimed to develop a robust [...] Read more.
Drones are increasingly vital in numerous fields, such as commerce, delivery services, and military operations. Therefore, it is essential to develop advanced systems for detecting and recognizing drones to ensure the safety and security of airspace. This paper aimed to develop a robust solution for detecting and recognizing drones and birds in airspace by combining a radar system and a visual imaging system, and contributed to this effort by demonstrating the potential of combining the two systems for drone detection and recognition. The results showed that this approach was highly effective, with a high overall precision and accuracy of 88.82% and 71.43%, respectively, and the high F1 score of 76.27% indicates that the proposed combination approach has great effectiveness in the performance. The outcome of this study has significant practical implications for developing more advanced and effective drone and bird detection systems. The proposed algorithm is benchmarked with other related works, which show acceptable performance compared with other counterparts. Full article
(This article belongs to the Special Issue Advanced Technology of Target Detection, Tracking, Imaging, and Recognition for Radar)
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11 pages, 1787 KiB  
Article
Unambiguous Direction Estimation and Localization of Two Unresolved Targets via Monopulse Radar
by Habib Rezaei, Mohammad Ali Sebt, Nadali Zarei and Goudarz Saadati Moghadam
Electronics 2022, 11(22), 3780; https://doi.org/10.3390/electronics11223780 - 17 Nov 2022
Cited by 1 | Viewed by 1454
Abstract
Traditional monopulse radar cannot resolve two closely spaced targets present in one resolution cell (range and Doppler) by means of the monopulse ratio. This study presents a closed-form solution to resolve the directions of arrival of two unresolved targets using a single snapshot [...] Read more.
Traditional monopulse radar cannot resolve two closely spaced targets present in one resolution cell (range and Doppler) by means of the monopulse ratio. This study presents a closed-form solution to resolve the directions of arrival of two unresolved targets using a single snapshot of four independent channels in phase comparison monopulse radar. If both targets have the same elevation or same azimuth direction, the proposed scheme cannot estimate their directions. To estimate the direction of such targets, an extra antenna is required. The impact of input noise power, the targets’ direction, and phase difference of the targets’ signal on the accuracy of angle estimation are also explained. The numerical simulation result validates the effectiveness of the presented scheme. Full article
(This article belongs to the Special Issue Advanced Technology of Target Detection, Tracking, Imaging, and Recognition for Radar)
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