Volume 14 Issue 4
Aug.  2025
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Article Navigation >  Journal of Radars  > 2025  >  14(4): 1071-1091
WANG Xianmei, LIU Xiangbo, REN Yuzheng, et al. Review of research on artificial intelligence-driven joint radar communication[J]. Journal of Radars, 2025, 14(4): 1071–1091. doi: 10.12000/JR24252
Citation: WANG Xianmei, LIU Xiangbo, REN Yuzheng, et al. Review of research on artificial intelligence-driven joint radar communication[J]. Journal of Radars, 2025, 14(4): 1071–1091. doi: 10.12000/JR24252
shu

Review of Research on Artificial Intelligence-driven Joint Radar Communication

DOI: 10.12000/JR24252 CSTR: 32380.14.JR24252
  • 1.

    School of Computer & Communication Engineering，University of Science and Technology Beijing, Beijing 100083, China

  • 2.

    China Electric Power Research Institute, Beijing 100192, China

Funds:  The National Natural Science Foundation of China (U22B2003), The Fundamental Research Funds for the Central Universities (FRF-TP-22-002C2), The National Key Laboratory of Wireless Communications Foundation (IFN20230201), The Xiaomi Fund of Young Scholar
More Information
  • Corresponding author: ZHANG Haijun, zhanghaijun@ustb.edu.cn
  • Received Date: 2024-12-16
  • Rev Recd Date: 2025-04-02
    • Available Online: 2025-04-07
  • Publish Date: 2025-04-25
    Abstract
  • Joint radar communication leverages resource-sharing mechanisms to improve system spectrum utilization and achieve lightweight design. It has wide applications in air traffic control, healthcare monitoring, and autonomous vehicles. Traditional joint radar communication algorithms often rely on precise mathematical modeling and channel estimation and cannot adapt to dynamic and complex environments that are difficult to describe. Artificial Intelligence (AI), with its powerful learning ability, automatically learns features from large amounts of data without the need for explicit modeling, thereby promoting the deep fusion of radar communication. This article provides a systematic review of the research on AI-driven joint radar communication. Specifically, the model and challenges of the joint radar communication system are first elaborated. On this basis, the latest research progress on AI-driven joint radar communication is summarized from two aspects: radar communication coexistence and dual-functional radar communication. Finally, the article is summarized, and the potential technical challenges and future research directions in this field are described.

     

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