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- The constant false alarm rate (CFAR) processing of radar signal is an important item of modern radar signal processing. 摘要雷达信号的恒虚警率(CFAR)处理是现代雷达信号处理的重要内容。
- A new constant false alarm rate(CFAR) detector(CMCAGO-CFAR) based on censored mean and cell averaging is presented in this paper. 文中基于删除平均(CM)方法和单元平均(CA)方法,提出了一种新的恒虚警检测器(CMCAGO-CFAR)。
- Firstly, the prescreening of images is made by the Constant False Alarm Rate (CFAR) detector, then followed by a morphological filter and clustering analysis. 首先利用CFAR检测、形态滤波和聚类分析对图像进行预筛选；
- The cooperation method in this system using blackboard, is explained.A CFAR (constant false alarm rate) method is used to fusion the distributed analysis result. 提出一种采用黑板模型进行模块间的协作的方法，一种基于恒虚警检验的数据融合方法，及在此基础上的激励反馈机制。
- Experiment shows methods in this paper are better than the two-parameter constant false alarm rate (CFAR) in detection validity and calculating quantity. 实际测试结果表明所提出的检测方法在检测效果和计算量方面都优于双参数恒虚警检测算法。
- constant false alarm rate (CFAR) 恒虚警
- low-threshold constant false alarm rate (CFAR) 低门限恒虚警
- constant false alarm rate (CFAR) detection 恒虚警检测
- Constant false alarm rate (CFAR) processor 恒虚警处理
- Moving Target Indicator Constant False Alarm Rate 活动目标指示器(接收机)恒定虚警率
- CFAR (constant false alarm rate) 恒虚警率
- CFAR( constant false alarm rate) 恒虚警
- CFAR(Constant False Alarm Rate)detection 恒虚警检测
- constant false alarm rate detection 恒虚警检测
- distributed constant false alarm rate detection 分布式CFAR检测
- Realization Of Constant False Alarm Rate Processing in Radar Clutter Background 雷达杂波环境恒虚警率处理的实现
- Constant False Alarm Rate(CFAR) 恒虚警
- constant false alarm rate 恒定虚警率
- Keywords Synthetic Aperture Radar;De-noising for speckle;target detection;Edge extraction;,,,,Classification;wavelet transform;adaptive Wiener filter;Robust estimate;Constant false alarm rate; 合成孔径雷达;相干斑抑制;目标检测;边缘提取;分类;小波变换;自适应维纳滤波;鲁棒估计;恒虚警;
- At a miss probability of 5%, the hybrid approach using UBM and T-cohort models reduce the false alarm rate to 0.5% compared to 2% for the baseline. 当错误拒绝率为5%25时;该方法可以获得0.;5%25的错误接受率;远远低于采用通用背景模型归一化方法的2%25。
