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提供一个cell-weraging constant false alarm rate (CA-CFAR)的算法，基于信号检测的方法。-We present a method for evaluating the performance of
cell-weraging constant false alarm rate (CA-CFAR) processors
which use the anplitude of echo signals rather than their
squ
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在检测与估计中 仿真给出检查概率与虚警概率的关系 Pfa=1-Q(z),Pd=1-Q(z-d),画不同d情况下Pfa与Pd的关系图-In the detection and estimation of simulation shows that the relationship between the check probability and false alarm probability Pfa = 1-Q (z), Pd = 1-Q (zd), PFA and Pd diagram Vid
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认知无线电能量检测。虚警概率一定，不同信噪比情况下检测概率不同-Cognitive radio energy detection. Certain probability of false alarm, the probability of detection of different circumstances different SNR
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在matlab上实现对恒虚预警处理的单元平均理，分析单元检测处理的检测性能-Achieve constant false alarm processing unit on the average processing matlab, performance analysis and detection unit detects processing
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,在强干扰中提取信号,不仅要求有一定的信噪比,而且必须对信号作恒虚警处理.在自动检测系统中,对一定的检测门限来说,当干扰电平增大几分贝,虚警率就会大大增加,致使显示器画面饱和或计算机过载,这时即使有足够大的信噪比,也不可能做出正确的判决.为此在自动检测雷达中,必须具备恒虚警处-, extracting signal in strong interference, not only requires a certain signal-to-noise ratio, and must make co
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认知无线中的能量检测，仿真了高斯信道和瑞利信道的检测概率、虚警概率与判决门限的关系曲线以及两者之间的关系曲线-The detection probability, false alarm probability and the decision threshold of the Gauss channel and the Rayleigh channel are simulated by the energy detection in cognitive radio.
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多重相关系数的ROC曲线，说明此算法的检测概率与虚警概率的性能比其他经典频谱感知算法更好(The ROC curves of multiple correlation coefficients show that the performance of detection probability and false alarm probability of this algorithm is better than that of other classical spectrum sensing
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CFAR实现sar图像船舰目标检测与识别的matlab代码(Constant false alarm algorithm CFAR to achieve the sea surface SAR images, ship target detection and identification of matlab code)
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在雷达信号检测中，当外界干扰强度变化时，雷达能自动调整其灵敏度，使雷达的虚警概率保持不变。具有这种特性的接收机称为恒虚警接收机。(In radar signal detection, the radar can automatically adjust its sensitivity when the external interference intensity changes, so that the radar's false alarm probability remains uncha
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基于高斯分布的CFAR检测算法(双参数CFAR检测)，输入一个确定的虚警概率，在满足一定虚警率的情况下对图像进行分割，实现目标与背景的分离。(The CFAR detection algorithm based on Gauss distribution (two-parameter CFAR detection) inputs a certain false alarm probability, and segmentes the image to achieve the separation
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二进制代码漏洞检测是一个重要的研究问题。针对当前基于静态分析的二进制代码漏洞检测系统普遍存在误报率高,检测粒度粗,且依赖专家经验等问题,提出了用库/API函数调用程序切片细粒度表示二进制程序,并引入深度学习技术,自动检测二进制程序中库/API函数调用相关漏洞,设计并实现了一个基于深度学习的二进制代码漏洞检测系统——BVDetector。该系统通过对二进制程序进行控制流和数据流分析,从程序中提取库/API函数调用的程序切片,并为程序切片添加有无漏洞的标签;然后将程序切片转换成符合漏洞检测深度学习模
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