An Overview of Waveform Optimization Methods for Cognitive Radar
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 th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
China Science Publishing & Media Ltd. (CSPM)
2019-10-01
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| Series: | Leida xuebao |
| Subjects: | |
| Online Access: | http://radars.ie.ac.cn/article/doi/10.12000/JR19072?viewType=HTML |
| _version_ | 1827616952205443072 |
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| author | CUI Guolong YU Xianxiang YANG Jing FU Yue KONG Lingjiang |
| author_facet | CUI Guolong YU Xianxiang YANG Jing FU Yue KONG Lingjiang |
| author_sort | CUI Guolong |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T09:34:10Z |
| format | Article |
| id | doaj.art-b69d007d92cb45efa6f327cb1d66e4db |
| institution | Directory Open Access Journal |
| issn | 2095-283X 2095-283X |
| language | English |
| last_indexed | 2024-03-09T09:34:10Z |
| publishDate | 2019-10-01 |
| publisher | China Science Publishing & Media Ltd. (CSPM) |
| record_format | Article |
| series | Leida xuebao |
| spelling | doaj.art-b69d007d92cb45efa6f327cb1d66e4db2023-12-02T02:39:14ZengChina Science Publishing & Media Ltd. (CSPM)Leida xuebao2095-283X2095-283X2019-10-018553755710.12000/JR19072An Overview of Waveform Optimization Methods for Cognitive RadarCUI Guolong0YU Xianxiang1YANG Jing2FU Yue3KONG Lingjiang4①(School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)①(School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)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)①(School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)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.http://radars.ie.ac.cn/article/doi/10.12000/JR19072?viewType=HTMLtarget detectioncognitive radarwaveform designoptimization theory |
| spellingShingle | CUI Guolong YU Xianxiang YANG Jing FU Yue KONG Lingjiang An Overview of Waveform Optimization Methods for Cognitive Radar Leida xuebao target detection cognitive radar waveform design optimization theory |
| title | An Overview of Waveform Optimization Methods for Cognitive Radar |
| title_full | An Overview of Waveform Optimization Methods for Cognitive Radar |
| title_fullStr | An Overview of Waveform Optimization Methods for Cognitive Radar |
| title_full_unstemmed | An Overview of Waveform Optimization Methods for Cognitive Radar |
| title_short | An Overview of Waveform Optimization Methods for Cognitive Radar |
| title_sort | overview of waveform optimization methods for cognitive radar |
| topic | target detection cognitive radar waveform design optimization theory |
| url | http://radars.ie.ac.cn/article/doi/10.12000/JR19072?viewType=HTML |
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