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Performance of the adaptive sidelobe blanker detection algorithm in homogeneous environments

机译:均质环境中自适应旁瓣遮断器检测算法的性能

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The adaptive sidelobe blanker (ASB) algorithm is a two-stage detector consisting of a first stage adaptive matched filter (AMF) detector followed by a second-stage detector called the adaptive coherence (or cosine) estimator (ACE). Only those data test cells that survive both detection thresholdings are declared signal (target) bearing. We provide exact novel closed-form expressions for the resulting probability of detection (PD) and false alarm (PFA) for the ASB algorithm and demonstrate that under homogeneous data conditions with no signal array response mismatch that (i) the ASB is a constant false alarm rate (CFAR) algorithm, (ii) the ASB has a higher or commensurate PD for a given PFA than both the AMF and the ACE, and (iii) the ASB has an overall performance that is commensurate with Kelly's (1986) benchmark generalized likelihood ratio test (GLRT). A compact statistical summary is derived providing distributions and dependencies among the GLRT, AMF, and the ACE decision statistics.
机译:自适应旁瓣消隐器(ASB)算法是两级检测器,由第一级自适应匹配滤波器(AMF)检测器和第二级检测器组成,第二级检测器称为自适应相干(或余弦)估计器(ACE)。只有那些在两个检测阈值下均幸存的数据测试单元才被声明为承载信号(目标)。我们为ASB算法的检测概率(PD)和错误警报(PFA)提供了精确的新型闭式表达式,并证明在没有信号阵列响应不匹配的同类数据条件下(i)ASB是恒定的错误警报率(CFAR)算法;(ii)给定PFA的ASB的PD高于或等于AMF和ACE的PD,并且(iii)ASB的总体性能与Kelly(1986)概括的基准相当似然比检验(GLRT)。得出紧凑的统计摘要,该摘要提供了GLRT,AMF和ACE决策统计信息之间的分布和依赖性。

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