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Electronics
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Advanced Radar Waveform Design and Intelligent Countermeasures in Integrated Radar...
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Advanced Radar Waveform Design and Intelligent Countermeasures in Integrated Radar and Communication Systems

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

Deadline for manuscript submissions: 15 September 2025 | Viewed by 869

Special Issue Editors

Dr. Xianxiang Yu

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: MIMO radar; waveform design; radar array signal processing; electronic countermeasure technology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jinfeng Hu

E-Mail Website
Guest Editor
Yangtze Delta Region Institute (Qu Zhou), University of Electronic Science and Technology of China, Quzhou 324003, China
Interests: adaptive processing; radar signal processing; intelligent signal processing; artificial intelligence and deep learning
Dr. Zhihui Li

E-Mail Website
Guest Editor
College of Electronic Engineering, National University of Defense Technology, Hefei 230039, China
Interests: radar signal processing; waveform design; radar countermeasure and artificial intelligence
Dr. Kang Li

E-Mail Website
Guest Editor
National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China
Interests: radar signal processing; application of artificial intelligence to ECM and ECCM
Dr. Jing Yang

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: waveform optimization design; array signal processing; anti-clutter interference; radar communication integration
Dr. Junhui Qian

E-Mail Website
Guest Editor
College of Communication Engineering, Chongqing University, 174 Sha Pingba, Chongqing 400044, China
Interests: MIMO radar; MIMO communication
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Waveforms are the means by which radars perceive the external environment and have always been a hot topic in the field of radar research. In recent years, radar waveform technology has been vigorously developed due to the diversity of targets, the diversification of radar tasks, and the complexity of the environment. At the same time, in order to effectively resist complex radar waveforms, electronic countermeasure technology has also been continuously developed. This Special Issue mainly focuses on the theme of radar waveform and countermeasures, summarizes the latest achievements in recent years, and aims to provide references for researchers.

Topics of interest include, but are not limited to:

  • Radar anti-interference waveform design technology;
  • Radar low-intercept waveform design technology;
  • Multi-function waveform design technology;
  • Radar beam anti-interference technology;
  • Interference waveform design technology;
  • Radar and interference dynamic countermeasure technology;
  • Electronic reconnaissance technology.

Dr. Xianxiang Yu
Prof. Dr. Jinfeng Hu
Dr. Zhihui Li
Dr. Kang Li
Dr. Jing Yang
Dr. Junhui Qian
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 anti-interference
  • waveform design
  • dynamic countermeasure
  • optimization method
  • electronic reconnaissance

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Published Papers (1 paper)

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Research

25 pages, 7158 KiB  
Article
Anti-Jamming Decision-Making for Phased-Array Radar Based on Improved Deep Reinforcement Learning
by Hang Zhao, Hu Song, Rong Liu, Jiao Hou and Xianxiang Yu
Electronics 2025, 14(11), 2305; https://doi.org/10.3390/electronics14112305 - 5 Jun 2025
Viewed by 530
Abstract
In existing phased-array radar systems, anti-jamming strategies are mainly generated through manual judgment. However, manually designing or selecting anti-jamming decisions is often difficult and unreliable in complex jamming environments. Therefore, reinforcement learning is applied to anti-jamming decision-making to solve the above problems. However, [...] Read more.
In existing phased-array radar systems, anti-jamming strategies are mainly generated through manual judgment. However, manually designing or selecting anti-jamming decisions is often difficult and unreliable in complex jamming environments. Therefore, reinforcement learning is applied to anti-jamming decision-making to solve the above problems. However, the existing anti-jamming decision-making models based on reinforcement learning often suffer from problems such as low convergence speeds and low decision-making accuracy. In this paper, a multi-aspect improved deep Q-network (MAI-DQN) is proposed to improve the exploration policy, the network structure, and the training methods of the deep Q-network. In order to solve the problem of the ϵ-greedy strategy being highly dependent on hyperparameter settings, and the Q-value being overly influenced by the action in other deep Q-networks, this paper proposes a structure that combines a noisy network, a dueling network, and a double deep Q-network, which incorporates an adaptive exploration policy into the neural network and increases the influence of the state itself on the Q-value. These enhancements enable a highly adaptive exploration strategy and a high-performance network architecture, thereby improving the decision-making accuracy of the model. In order to calculate the target value more accurately during the training process and improve the stability of the parameter update, this paper proposes a training method that combines n-step learning, target soft update, variable learning rate, and gradient clipping. Moreover, a novel variable double-depth priority experience replay (VDDPER) method that more accurately simulates the storage and update mechanism of human memory is used in the MAI-DQN. The VDDPER improves the decision-making accuracy by dynamically adjusting the sample size based on different values of experience during training, enhancing exploration during the early stages of training, and placing greater emphasis on high-value experiences in the later stages. Enhancements to the training method improve the model’s convergence speed. Moreover, a reward function combining signal-level and data-level benefits is proposed to adapt to complex jamming environments, which ensures a high reward convergence speed with fewer computational resources. The findings of a simulation experiment show that the proposed phased-array radar anti-jamming decision-making method based on MAI-DQN can achieve a high convergence speed and high decision-making accuracy in environments where deceptive jamming and suppressive jamming coexist. Full article
(This article belongs to the Special Issue Advanced Radar Waveform Design and Intelligent Countermeasures in Integrated Radar and Communication Systems)
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