| 基于雷达知识库的知识辅助恒虚警检测算法 |
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| 引用本文： | 卢术平,宋海洋,易伟,孔令讲,杨晓波.基于雷达知识库的知识辅助恒虚警检测算法[J].现代雷达,2017(6):46-49. |
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| 作者姓名： | 卢术平 宋海洋 易伟 孔令讲 杨晓波 |
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| 作者单位： | 电子科技大学电子工程学院,电子科技大学电子工程学院,电子科技大学电子工程学院,电子科技大学电子工程学院,电子科技大学电子工程学院 |
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| 基金项目： | 国家自然科学基金资助项目(61301266, 61170868,61201276); 中国中央大学基本研究基金资助项目( ZYGX2012Z001, ZYGX2013J012, ZYGX2014J013,ZYGX2014Z005); 中国博士后科学基金资助项目(2014M550465); 新世纪优秀人才支持计划资助项目(A1098524023901001063) |
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| 摘 要： | 在非均匀杂波背景下,传统的恒虚警检测算法,比如CA鄄CFAR,所选择的参考单元与待检测单元往往无法满足独立同分布的条件,导致背景杂波功率水平的估计值存在偏差,使得检测性能降低。针对上述问题,文中提出了一种基于雷达知识库的知识辅助恒虚警检测算法。首先,利用雷达环境知识来构建动态的雷达知识库;然后,利用雷达知识库中的先验信息来辅助参考单元的选择,提高背景杂波功率水平的估计准确性,从而降低非均匀背景带来的影响;最后,利用线性调频连续波雷达采集的实测数据对该算法性能进行了验证。结果表明:在非均匀杂波环境下,基于雷达知识库的知识辅助恒虚警检测算法比传统算法有更好的检测性能。
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| 关 键 词： | 雷达知识库 知识辅助 恒虚警检测 |
Knowledge-aided CFAR Algorithm Based on Radar Knowledge Base |
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| LU Shuping,SONG Haiyang,YI Wei,KONG Lingjiang and YANG Xiaobo.Knowledge-aided CFAR Algorithm Based on Radar Knowledge Base[J].Modern Radar,2017(6):46-49. |
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| Authors: | LU Shuping SONG Haiyang YI Wei KONG Lingjiang and YANG Xiaobo |
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| Affiliation: | School of Electronic Engineering, University of Electronic Science and Technology of China,School of Electronic Engineering, University of Electronic Science and Technology of China,School of Electronic Engineering, University of Electronic Science and Technology of China,School of Electronic Engineering, University of Electronic Science and Technology of China and School of Electronic Engineering, University of Electronic Science and Technology of China |
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| Abstract: | Since the selected reference cells and cell under test usually cannot meet the assumption of statistically independent identically distribution, the conventional constant false alarm rate (CFAR) detectors, such as CA-CFAR, suffer considerable performance degradation in heterogeneous background. In this case, a new knowledge-based CFAR (KB-CFAR) detector based on radar knowledge base is proposed to reduce the impact of heterogeneous environment. The proposed KB-CFAR detector exploits auxiliary knowledge in the radar knowledge base to select reference cells, improves the estimation of background clutter power level, and reduces the impact of heterogeneous environment. The performance of the new KB-CFAR detector is analyzed by the real radar data collected by a linear frequency-modulated continues wave (LFMCW) radar. The results show that the new detector performs better than the conventional CFAR detectors in the aspects of controlling of false alarm points and development of detection probability in heterogeneous environment. |
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| Keywords: | radar knowledge base knowledge-based CFAR detection |
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