面向不同雷达任务的认知波形优化综述
A Review on Cognitive Waveform Optimization for Different Radar Missions
查看参考文献218篇
文摘
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传统雷达系统的发射机与接收机采用开环工作模式,在动态复杂环境下探测目标时缺乏灵活性和稳健性.借鉴生物认知学习过程,认知雷达可以感知动态环境和目标信息,通过发射和接收端闭环反馈控制,实现全自适应探测和信号处理.本文介绍了认知雷达波形优化的基本框架,递进地梳理了面向检测、跟踪、成像、分类任务以及抗干扰认知波形优化的主要研究内容和研究进展,为面向单一任务及联合多任务的波形优化技术研究提供了纵向和横向的对比视角.在已有研究的基础上,本文分析了认知雷达波形优化的优势和挑战,指出认知波形优化技术中潜在的研究方向,包括知识有效性评价、人工智能认知波形优化、通用度量准则、知识辅助的高效优化算法等方面. |
其他语种文摘
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The transmitter and receiver of traditional radar system use open-loop mode, which is lack of flexibility and robustness when sensing targets in dynamic environment. Based on biological cognitive learning process, cognitive radar can sense dynamic environment and target information, and realize fully adaptive processing by closed-loop feedback control of transmitter and receiver. In this paper, the basic framework of cognitive radar waveform optimization is introduced, and the main research contents and progress of cognitive waveform optimization oriented to detection, tracking, imaging, classification and anti-jamming are summarized, which provides a vertical and horizontal comparative perspective for waveform optimization technology research oriented to single mission and joint multi-missions. On the basis of existing research, analysis of the cognitive radar waveform optimization advantages and challenges, points out that the cognitive potential research area in waveform optimization techniques, including knowledge validity evaluation, artificial intelligence based cognitive waveform optimization, General metrics, knowledge-aided optimization algorithms, etc. |
来源
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电子学报
,2022,50(3):726-752 【核心库】
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DOI
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10.12263/DZXB.20211068
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关键词
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认知雷达
;
波形优化
;
波形设计
;
雷达任务
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地址
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国防科技大学电子科学学院, 湖南, 长沙, 410073
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语种
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中文 |
文献类型
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综述型 |
ISSN
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0372-2112 |
学科
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电子技术、通信技术 |
基金
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国家自然科学基金
;
中国博士后科学基金第3批特别资助
;
湖南省自然科学基金杰出青年基金
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文献收藏号
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CSCD:7206922
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参考文献 共
218
共11页
|
1.
Gini F.
Knowledge-Based Radar Detection, Tracking, and Classification,2007
|
CSCD被引
1
次
|
|
|
|
2.
Jakobsen L. Convergent acoustic field of view in echolocating bats.
Nature,2013,493:93-96
|
CSCD被引
1
次
|
|
|
|
3.
Haykin S. Cognitive radar: A way of the future.
IEEE Signal Processing Magazine,2006,23(1):30-40
|
CSCD被引
152
次
|
|
|
|
4.
Gurbuz S Z. An overview of cognitive radar: Past, present, and future.
IEEE Aerospace and Electronic Systems Magazine,2019,34(12):6-18
|
CSCD被引
7
次
|
|
|
|
5.
Horne C. Proposed ontology for cognitive radar systems.
IET Radar, Sonar & Navigation,2018,12(12):1363-1370
|
CSCD被引
3
次
|
|
|
|
6.
黎湘. 认知雷达及其关键技术研究进展.
电子学报,2012,40(9):1863-1870
|
CSCD被引
41
次
|
|
|
|
7.
左群声.
认知雷达导论,2017
|
CSCD被引
4
次
|
|
|
|
8.
Gini F.
Waveform Design and Diversity for Advanced Radar Systems,2012
|
CSCD被引
12
次
|
|
|
|
9.
Wicks M C. A brief history of waveform diversity.
2009 IEEE Radar Conference,2009:1-6
|
CSCD被引
1
次
|
|
|
|
10.
Blunt S D. Overview of radar waveform diversity.
IEEE Aerospace and Electronic Systems Magazine,2016,31(11):2-42
|
CSCD被引
19
次
|
|
|
|
11.
崔国龙. 认知雷达波形优化设计方法综述.
雷达学报,2019,8(5):537-557
|
CSCD被引
27
次
|
|
|
|
12.
王璐璐. 雷达目标检测的最优波形设计综述.
雷达学报,2016,5(5):487-498
|
CSCD被引
15
次
|
|
|
|
13.
王建涛.
面向参数估计的认知雷达自适应发射波形优化技术研究,2014
|
CSCD被引
1
次
|
|
|
|
14.
杨威.
基于有限集统计学理论的机动目标联合检测、跟踪与分类技术研究,2012
|
CSCD被引
1
次
|
|
|
|
15.
Cochran D. Waveform libraries.
IEEE Signal Processing Magazine,2009,26(1):12-21
|
CSCD被引
11
次
|
|
|
|
16.
Zhang J D. 5Multi-objective waveform design for cognitive radar.
Proceedings of 2011 IEEE CIE International Conference on Radar,2011:580-583
|
CSCD被引
1
次
|
|
|
|
17.
Liu W X. Optimal sparse waveform design for HFSWR system.
2007 International Waveform Diversity and Design Conference,2007:127-130
|
CSCD被引
3
次
|
|
|
|
18.
Wang G H. Sparse frequency transmit waveform design with soft power constraint by using PSO algorithm.
2008 IEEE Radar Conference,2008:1-4
|
CSCD被引
3
次
|
|
|
|
19.
Setlur P. Projected gradient waveform design for fully adaptive radar STAP.
2015 IEEE Radar Conference(RadarCon),2015:1704-1709
|
CSCD被引
1
次
|
|
|
|
20.
Sun Y. Majorization-minimization algorithms in signal processing, communications, and machine learning.
IEEE Transactions on Signal Processing,2017,65(3):794-816
|
CSCD被引
49
次
|
|
|
|
|