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Radar Signal and Data Processing with Applications, 2nd Edition
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School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Prof. Dr. Yulin Huang
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Dr. Deqing Mao
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Dr. Yanki Aslan
Microwave Sensing, Signals and Systems Section, Delft University of Technology, 2628 Delft, The Netherlands
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Radar Signal and Data Processing with Applications, 2nd Edition

Abstract submission deadline
31 August 2025
Manuscript submission deadline
30 November 2025
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Topic Information

Dear Colleagues,

Radars perform a significant role in the airborne, vehicle, shipborne, or surface deformation monitoring fields because of their all-day and all-weather abilities. Many studies have been carried out to improve the sensing precision of radars, such as multi-dimensional sensing, multi-domain detection, and super-resolution methods, among others. However, it is difficult to fully extract information using traditional data processing approaches. With the development of artificial intelligence, radar performance has been improved. Therefore, there is a need to further explore new AI technologies or advanced algorithms with high precision and performance. This Topic collection is open to researchers and authors who want to submit works in the fields of radar applications, new methods in radar signal processing, novel approaches to improving radar performance, and AI methods in radars. We are looking forward to submissions on topics of interest including but not limited to the following:

  • Radar signal processing;
  • AI in radar;
  • Radar imaging;
  • Super-resolution radar;
  • Airborne radar;
  • Vehicle radar;
  • Shipborne radar;
  • Surface deformation radar;
  • Biomedical radar to include vital sign monitoring and other biomedical radar applications;
  • Antennas for radar applications;
  • mmWave radars;
  • New radar applications.

Prof. Dr. Yin Zhang
Prof. Dr. Yulin Huang
Dr. Deqing Mao
Dr. Yanki Aslan
Topic Editors

Keywords

  • radar system
  • radar signal processing
  • radar performance improvement
  • AI in radar
  • radar applications

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci 		2.5 5.3 2011 18.4 Days CHF 2400 Submit
Remote Sensing
remotesensing 		4.2 8.3 2009 23.9 Days CHF 2700 Submit
Sensors
sensors 		3.4 7.3 2001 18.6 Days CHF 2600 Submit
Signals
signals 		- 3.2 2020 28.3 Days CHF 1000 Submit
Electronics
electronics 		2.6 5.3 2012 16.4 Days CHF 2400 Submit

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Published Papers (1 paper)

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16 pages, 746 KiB  
Article
A Multi-Receiver Pulse Deinterleaving Method Based on SSC-DBSCAN and TDOA Mapping
by Jie Xue, Binbin Su, Yongcai Liu and Jin Meng
Electronics 2025, 14(9), 1833; https://doi.org/10.3390/electronics14091833 (registering DOI) - 29 Apr 2025
Abstract
Deinterleaving pulses of various pulse repetition interval (PRI) modulation modes constitute a vital and challenging task for an electronic measures system (ESM). A deinterleaving method based on multi-receiver time-difference-of-arrival (TDOA) is proposed in this paper. Firstly, this paper theoretically analyzes the distribution feature [...] Read more.
Deinterleaving pulses of various pulse repetition interval (PRI) modulation modes constitute a vital and challenging task for an electronic measures system (ESM). A deinterleaving method based on multi-receiver time-difference-of-arrival (TDOA) is proposed in this paper. Firstly, this paper theoretically analyzes the distribution feature of TDOA, providing the basis of deinterleaving. Then, a SSC (Sorting Skipping Clustering)-DBSCAN algorithm is proposed to achieve TDOA clustering by pre-sorting and traversing key points, which reduces the computational complexity. The TDOA mapping algorithm is further proposed to separate pulses and eliminate Cross-Pulse TDOAs simultaneously based on a one-time clustering result, which can significantly decrease the false alarm rate while avoiding clustering TDOA repeatedly. Simulation results show that the proposed method is capable of deinterleaving pulses of various PRI modulation modes and the performance remains excellent under multiple parameter settings. The running time and the false alarm rate have been reduced by at least 66% and 17%, respectively, compared with the existing methods. Full article
(This article belongs to the Topic Radar Signal and Data Processing with Applications, 2nd Edition)
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