认知雷达波形优化设计方法综述

崔国龙,  余显祥,  杨婧,  付月,  孔令讲

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文章导航 > 雷达学报  > 2019 >  8(5): 537-557
崔国龙, 余显祥, 杨婧, 等. 认知雷达波形优化设计方法综述[J]. 雷达学报, 2019, 8(5): 537–557. doi: 10.12000/JR19072
引用本文: 崔国龙, 余显祥, 杨婧, 等. 认知雷达波形优化设计方法综述[J]. 雷达学报, 2019, 8(5): 537–557. doi: 10.12000/JR19072
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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
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认知雷达波形优化设计方法综述

doi: 10.12000/JR19072
  • 1.

    电子科技大学信息与通信工程学院 成都 611731

  • 2.

    上海汽车集团股份有限公司 上海 201804

基金项目: 国家自然基金(61771109, 61871080)，长江学者计划，111计划(B17008)，中央高校基本业务费(2672018ZYGX2018J016)
详细信息
    作者简介:

    崔国龙(1982–)，男，安徽人，电子科技大学教授，博士生导师，《雷达学报》编委。研究方向包括：最优化理论和算法、雷达目标检测理论、波形多样性以及阵列信号处理等。E-mail: cuiguolong@uestc.edu.cn

    余显祥(1991–)，男，四川人，电子科技大学博士研究生，研究方向包括：雷达波形设计与处理、最优化理论算法以及阵列信号处理等。E-mail: xianxiangy@gmail.com

    杨　婧(1995–)，女，河北人，电子科技大学博士研究生，研究方向包括：雷达波形设计与处理、最优化理论算法以及阵列信号处理等。E-mail: yangjinguestc@163.com

    付　月(1992–)，女，湖北人，上海汽车集团股份有限公司技术中心工程师，研究方向包括：雷达波形设计、基于智能驾驶汽车的毫米波雷达设计与开发、毫米波雷达SLAM应用开发等。E-mail: 18482205102@163.com

    孔令讲(1974–)，男，河南人，电子科技大学教授，博士生导师，《雷达学报》编委。研究方向包括：新体制雷达、统计信号处理、优化理论和算法、雷达信号处理、非合作信号处理技术和自适应阵列信号处理等。E-mail: ljkong@uestc.edu.cn

    通讯作者:

    崔国龙　cuiguolong@uestc.edu.cn

  • 责任主编：崔琛 Corresponding Editor: CUI Chen

  • 中图分类号: TN958

An Overview of Waveform Optimization Methods for Cognitive Radar

  • 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

    摘要
  • 摘要: 认知雷达通过借鉴蝙蝠的认知学习过程，感知战场环境信息并反馈至发射机，从而实现自适应探测和处理，是未来雷达智能化发展的重点方向。其中如何充分利用目标与环境先验信息，设计雷达波形以提高目标检测、跟踪以及抗干扰等性能是认知雷达发展的难点和重点。该文针对不同干扰环境、目标模型、天线配置(如：单发单收(SISO)和多发多收(MIMO))等的波形设计关键要素及主要思路进行了总结梳理，并从不同干扰与目标知识的利用角度，对近几年代表性的认知波形设计文献进行介绍和归纳，旨在为以后的研究提供参考和依据。

     

    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.

     

    • HTML全文

  • 图  1  认知雷达系统原理框图[1]

    Figure  1.  Schematic diagram of cognitive radar system[1]

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    图  2  静止点目标回波检测模型

    Figure  2.  Signal model for still point target detection

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    图  3  不同相似性参数下的能量谱密度、信干噪比、阻带电平与自相关函数[17]

    Figure  3.  ESD, SINR, stopband level and autocorrelation function versus different $\epsilon $[17]

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    图  4  检测概率随着不同目标功率变化图[23]

    Figure  4.  Detection probability versus target power[23]

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    图  5  不同相似性参数下的模糊函数[23]

    Figure  5.  Ambiguity function versus different $\epsilon $[23]

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    图  6  最坏信干噪比随着杂波不确定参数变化[57]

    Figure  6.  Worst SINR versus uncertain parameter[57]

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    图  7  互模糊函数[72]

    Figure  7.  Cross ambiguity function[72]

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    图  8  不同离散相位个数下的自相关函数与能量谱密度[77]

    Figure  8.  Autocorrelation and ESD for different M[77]

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    图  9  距离-多普勒平面[86]

    Figure  9.  Range-Doppler plane[86]

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    图  10  相关函数[87]

    Figure  10.  Correlation function[87]

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    图  11  能量谱密度[101]

    Figure  11.  ESD[101]

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    图  12  不同相似性参数下的方向图[108]

    Figure  12.  Beampattern for different similarity parameters[108]

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