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Remote Sensing
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Technical Developments in Radar—Processing and Application (2nd Edition)
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Technical Developments in Radar—Processing and Application (2nd Edition)

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

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 3352

Special Issue Editors

Dr. Xiaolong Chen

E-Mail Website
Guest Editor
Department of Electronic Information Engineering, Naval Aviation University, Yantai 246000, China
Interests: radar signal processing; AI for radar target detection
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shuwen Xu

E-Mail Website
Guest Editor
School of Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: radar; radar signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Luca Pallotta

E-Mail Website
Guest Editor
School of Engineering, University of Basilicata, 85100 Potenza, Italy
Interests: radar; radar signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the development of new radar systems such as multi-input-multiple-output (MIMO) radar, frequency diversity array (FDA) radar, networked radar, cognitive radar, ubiquitous radar, distributed radar, passive radar, etc., radar data acquisition methods have gradually evolved from single-band, single-polarization, single-angle, etc., to multi-frequency, multi-polarization, multi-angle acquisitions. The multi-dimensional signal processing for radar is gradually developed to meet the needs of complex radar target detection, parameter estimation, target tracking, and target recognition. Improvement of radar processing capability is the core issue for the application of the novel radar systems. Therefore, the research on signal and data processing, classification, and remote sensing has extremely important theoretical value and practical significance.

The objective of this Special Issue is to provide a forum for radar researchers to present their recent advances in the field. Radar processing (signal, data, and images processing) is closely related to remote sensing.

Suggested themes and article types for submissions.

  1. Radar waveform design and optimization technology;
  2. Radar array processing technology;
  3. Radar polarization processing technology;
  4. Radar anti-interference technology;
  5. Radar target detection technology;
  6. Radar target tracking technology;
  7. Radar target recognition technology;
  8. Radar imaging technology;
  9. AI for radar processing;
  10. Novel radar system and processing.

Dr. Xiaolong Chen
Prof. Dr. Shuwen Xu
Dr. Luca Pallotta
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 detection
  • radar tracking
  • radar target recognition
  • radar signal processing
  • radar remote sensing
  • deep learning for radar

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (5 papers)

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Research

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22 pages, 1869 KiB  
Article
Closely Spaced Multi-Target Association and Localization Using BR and AOA Measurements in Distributed MIMO Radar Systems
by Zehua Yu, Ziyang Jin, Ting Sun, Jinshan Ding, Jun Li and Qinghua Guo
Remote Sens. 2025, 17(6), 992; https://doi.org/10.3390/rs17060992 - 12 Mar 2025
Viewed by 425
Abstract
This work addresses the issue of closely spaced multi-target localization in distributed MIMO radars using bistatic range (BR) and angle of arrival (AOA) measurements. We propose a two-step method, decomposing the problem into measurement association and individual target localization. The measurement association poses [...] Read more.
This work addresses the issue of closely spaced multi-target localization in distributed MIMO radars using bistatic range (BR) and angle of arrival (AOA) measurements. We propose a two-step method, decomposing the problem into measurement association and individual target localization. The measurement association poses a significant challenge, particularly when targets are closely spaced along with the existence of both false alarms and missed alarms. To tackle this challenge, we formulate it as a clustering problem and we propose a novel clustering algorithm. By carefully defining the distance metric and the set of neighboring estimated points (EPs), our method not only produces accurate measurement association, but also provides reliable initial values for the subsequent individual target localization. Single-target localization remains challenging due to the involved nonlinear and nonconvex optimization problems. To address this, we formulate the objective function as a form of the product of certain local functions, and we design a factor graph-based iterative message-passing algorithm. The message-passing algorithm dynamically approximates the complex local functions involved in the problem, delivering excellent performance while maintaining low complexity. Extensive simulation results demonstrate that the proposed method not only achieves highly efficient association but also outperforms state-of-the-art algorithms and exhibits superior consistency with the Cramer–Rao lower bound (CRLB). Full article
(This article belongs to the Special Issue Technical Developments in Radar—Processing and Application (2nd Edition))
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22 pages, 8213 KiB  
Article
Optimization of Orthogonal Waveform Using Memetic Algorithm with Iterative Greedy Code Search
by Wanbin Wang, Lu Qian and Yun Zhou
Remote Sens. 2025, 17(5), 856; https://doi.org/10.3390/rs17050856 - 28 Feb 2025
Viewed by 434
Abstract
The orthogonality of transmitted waveforms is an important factor affecting the performance of MIMO radar systems. The orthogonal coded signal is a commonly adopted waveform in MIMO radar, and its orthogonality depends on the used orthogonal discrete code sequence set (ODCSs). Among existing [...] Read more.
The orthogonality of transmitted waveforms is an important factor affecting the performance of MIMO radar systems. The orthogonal coded signal is a commonly adopted waveform in MIMO radar, and its orthogonality depends on the used orthogonal discrete code sequence set (ODCSs). Among existing optimization algorithms for ODCSs, the results designed by the greedy code search-based memetic algorithm (MA-GCS) have exhibited the best autocorrelation and cross-correlation properties observed so far. Based on MA-GCS, we propose a novel hybrid algorithm called the memetic algorithm with iterative greedy code search (MA-IGCS). Extensions involve replacing the greedy code search used in MA-GCS with a more efficient approach, iterative greedy code search. Furthermore, we propose an “individual uniqueness strategy” and incorporate it into our algorithm to preserve population diversity throughout iteration, thereby preventing premature stagnation and ensuring the continued pursuit of feasible solutions. Finally, the design results of our algorithm are compared with the MA-GCS. Experimental results demonstrate that the MA-IGCS exhibits superior search capability and generates more favorable design results than the MA-GCS. Full article
(This article belongs to the Special Issue Technical Developments in Radar—Processing and Application (2nd Edition))
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27 pages, 36300 KiB  
Article
Maritime Target Radar Detection and Tracking via DTNet Transfer Learning Using Multi-Frame Images
by Xiaoyang He, Xiaolong Chen, Xiaolin Du, Xinghai Wang, Shuwen Xu and Jian Guan
Remote Sens. 2025, 17(5), 836; https://doi.org/10.3390/rs17050836 - 27 Feb 2025
Viewed by 835
Abstract
Traditional detection and tracking methods struggle with the complex and dynamic maritime environment due to their poor generalization capabilities. To address this, this paper improves the YOLOv5 network by integrating Transformer and a Convolutional Block Attention Module (CBAM) with the multi-frame image information [...] Read more.
Traditional detection and tracking methods struggle with the complex and dynamic maritime environment due to their poor generalization capabilities. To address this, this paper improves the YOLOv5 network by integrating Transformer and a Convolutional Block Attention Module (CBAM) with the multi-frame image information obtained from radar scans. It proposes a detection and tracking method based on the Detection Tracking Network (DTNet), which leverages transfer learning and the DeepSORT tracking algorithm, enhancing the detection capabilities of the model across various maritime environments. First, radar echoes are preprocessed to create a dataset of Plan Position Indicator (PPI) images for different marine conditions. An integrated network for detecting and tracking maritime targets is then designed, utilizing the feature differences between moving targets and sea clutter, along with the coherence of inter-frame information for moving targets, to achieve multi-target detection and tracking. The proposed method was validated on real maritime targets, achieving a precision of 99.06%, which is a 7.36 percentage point improvement over the original YOLOv5, demonstrating superior detection and tracking performance. Additionally, the impact of maritime regions and weather conditions is discussed, showing that, when transferring from Region I to Regions II and III, the precision reached 92.2% and 89%, respectively, and, when facing rainy weather, although there was interference from the sea clutter and rain clutter, the precision was still able to reach 82.4%, indicating strong generalization capabilities compared to the original YOLOv5 network. Full article
(This article belongs to the Special Issue Technical Developments in Radar—Processing and Application (2nd Edition))
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24 pages, 16264 KiB  
Article
Beacon-Based Phased Array Antenna Calibration for Passive Radar
by José P. González-Coma, Rubén Nocelo López, José M. Núñez-Ortuño and Francisco Troncoso-Pastoriza
Remote Sens. 2025, 17(3), 490; https://doi.org/10.3390/rs17030490 - 30 Jan 2025
Cited by 1 | Viewed by 846
Abstract
Passive radar has drawn a lot of attention due to its applications across military and civilian sectors. Under this working paradigm, the utilization of antenna arrays is instrumental, as it increases the signal quality and enables precise target positioning. These promising features rely, [...] Read more.
Passive radar has drawn a lot of attention due to its applications across military and civilian sectors. Under this working paradigm, the utilization of antenna arrays is instrumental, as it increases the signal quality and enables precise target positioning. These promising features rely, however, on the precise calibration of the antenna array, as the different hardware components introduce impairments that compromise the beamforming capabilities of the system. We propose a technique that employs a low-power external beacon signal to produce precise information about the target location, avoiding the angular ambiguities present in other solutions in the literature. The experimental results demonstrate the method’s ability to effectively correct the amplitude and phase inconsistencies while compensating for frequency drifts, enabling beamforming capabilities and direction-of-arrival estimation. Among the tested beacon waveforms, the pseudo-random noise-based signals proved the most robust, especially in low-power scenarios. Additionally, the method was validated in a passive radar setup, where it successfully detected a vessel using opportunistic signals. These findings highlight the method’s potential to enhance passive radar performance while maintaining a low probability of detection, a key aspect in military applications, as well as its applicability to civilian purposes, such as infrastructure monitoring, environmental observation, and traffic management. Full article
(This article belongs to the Special Issue Technical Developments in Radar—Processing and Application (2nd Edition))
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17 pages, 2800 KiB  
Technical Note
A Novel Method for PolISAR Interpretation of Space Target Structure Based on Component Decomposition and Coherent Feature Extraction
by Zhuo Chen, Zhiming Xu, Xiaofeng Ai, Qihua Wu, Xiaobin Liu and Jianghua Cheng
Remote Sens. 2025, 17(6), 1079; https://doi.org/10.3390/rs17061079 - 19 Mar 2025
Viewed by 280
Abstract
Inverse Synthetic Aperture Radar (ISAR) serves as a valuable instrument for surveillance of space targets. There has been a great deal of research on space target identification using ISAR. However, the polarization characteristics of space target components are rarely studied. Polarimetric Inverse Synthetic [...] Read more.
Inverse Synthetic Aperture Radar (ISAR) serves as a valuable instrument for surveillance of space targets. There has been a great deal of research on space target identification using ISAR. However, the polarization characteristics of space target components are rarely studied. Polarimetric Inverse Synthetic Aperture Radar (PolISAR) comprises two information dimensions, namely, polarization and image, enabling a more comprehensive understanding of target structures. This paper proposes a space target structure polarization interpretation method based on component decomposition and PolISAR feature extraction. The proposed method divides the target into components at the stage of modeling. Subsequently, electromagnetic calculations are performed for each component. The names of these components are used to label the dataset. Multiple polarization decomposition techniques are applied and many polarization features are obtained. The mapping correlations between the interpreted results and authentic target structures are improved through preferential selection of polarization features. Ultimately, the method is validated through analysis of simulation and anechoic chamber measurement data. The results show that the proposed method exhibits a more intuitive correlation with the authentic target structures compared to traditional polarized interpretation methods based on Cameron decomposition. Full article
(This article belongs to the Special Issue Technical Developments in Radar—Processing and Application (2nd Edition))
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