Volume 8 Issue 5
Oct.  2019
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CUI Guolong, YU Xianxiang, YANG Jing, et al. An overview of waveform optimization methods for cognitive radar[J]. Journal of Radars, 2019, 8(5): 537–557. doi: 10.12000/JR19072
Citation: CUI Guolong, YU Xianxiang, YANG Jing, et al. An overview of waveform optimization methods for cognitive radar[J]. Journal of Radars, 2019, 8(5): 537–557. doi: 10.12000/JR19072
shu

An Overview of Waveform Optimization Methods for Cognitive Radar

doi: 10.12000/JR19072
  • 1.

    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

  • 2.

    SAIC Motor Corporation Limited, Shanghai 201804, China

Funds:  The National Natural Science Foundation of China (61771109, 61871080), The Changjiang Scholar Program, 111 Project (B17008), The Fundamental Research Funds for the Central Universities (2672018ZYGX2018J016)
More Information
  • Corresponding author: CUI Guolong, cuiguolong@uestc.edu.cn
  • Received Date: 2019-08-01
  • Rev Recd Date: 2019-10-06
    • Available Online: 2019-10-18
  • Publish Date: 2019-10-01
    Abstract
  • Cognitive radar can sense the battlefield environment and feed this information back to a transmitter by imitating the cognitive learning process of bats to enable self-adaptive detection and processing, which are vital for the future intelligent development of radar. Therein, full utilization of the prior information of the target and environment to design radar waveform for improving the performance of target detection, tracking, and anti-jamming is difficult and has been the focus of cognitive radar development. Therefore, based on different jamming environments, target models, and antenna configurations (e.g., Single Input Single Output (SISO) and Multiple Inputs Multiple Outputs (MIMO)), this study summarizes the key elements and main ideas of waveform design. Furthermore, this study lists the related literature on representativeness from the viewpoint of the use of different jamming environments and target models, aiming at providing reference and basis for cognitive waveform design research in the future.

     

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