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| 论文题目 |
基于自动删除算法的恒虚警检测器 |
| 论文题目（英文） |
CFAR Detector Based on Automatic Censoring Algorithm |
| 作者 |
曲超 |
| 发表年度 |
2008 |
| 卷 |
23 |
| 期 |
5 |
| 页码 |
516-520 |
| 期刊名称 |
数据采集与处理 |
| 摘要 |
为了提高恒虚警检测器在均匀背景中的检测性能及增强对干扰的鲁棒性，基于自动删除单元平均(ACCA)方法提出一种新的恒虚警检测器(MACCA)，它的前沿和后沿滑窗均采用ACCA算法产生局部估计，再对二者求和得到背景功率水平估计，从而设置自适应检测门限。在Swerling II型目标假设下，推导出MACCA—CFAR在均匀背景下虚警概率尸h和检测概率Pa的解析表达式分别针对均匀背景和非均匀背景分析了MACCA的性能，并与其他现有方案进行了比较。结果表明MACCA继承ACCA优点的同时，有效地提高了在杂波边缘环境下的虚警控制能力，它的虚警尖峰比ACCA少了近一个数量级，并且样本排序时问只有ACCA的一半。
关键词：雷达；检测；恒虚警率；自动删除单元平均；排序数据方差 |
| 摘要_英文 |
To increase the detector robust performance，a new constant false alarm rate(CFAR)detector based on the automatic censored cell averaging(ACCA)一MACCA is proposed．It takes the sum of two ACCA locaI estimations as a noise power estimation．For the new CFAR detector analytic expressions of the false alarm rate detection probabilities are obtained in homogeneous background．Compared with other schemes，simulation results show that MACCA has the advantage of ACCA，and improves the control ability of the false alarm rate against the clutter edge．The spike of the false alarm rate of MACCA nearly decreases an order of the magnitude than that of ACCA，and the sample sorting time of MACCA is only an half of ACCA．
Key words：radar；detection；constantfalse alarm rate(CFAR)；automatic censored eell averaging(ACCA)；ordered data variability(ODV) |
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