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A comparison of two adaptive detection schemes

机译:两种自适应检测方案的比较

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Two schemes for adaptive detection are compared: Kelly's generalized likelihood ratio test (GLRT) and the mean level adaptive detector (MLAD). Detection performance, PD, is predicted for the two schemes under the assumptions that the input noises are zero-mean complex Gaussian random variables that are temporally independent but spatially correlated; and the amplitude of the desired signal is Rayleigh distributed. PD is computed as a function of the false alarm probability, the number of input channels, the number of independent samples per channel, and the matched filtered output signal-to-noise (S/N) power ratio. In this analysis the GLRT is shown to have better detection performance than the MLAD. The difference in detection performance increases as one uses fewer input samples. However, the required number of samples necessary to have only a 3 dB detection loss for both detection schemes is approximately the same. This is significant since for the present, the MLAD is considerably less complex to implement than the GLRT
机译:比较了两种自适应检测方案:凯利广义似然比检验(GLRT)和平均水平自适应检测器(MLAD)。在输入噪声是零均值复杂高斯随机变量的假设下,预测了这两种方案的检测性能PD,这些变量在时间上是独立的,但在空间上相关。期望信号的幅度是瑞利分布的。 PD是根据虚警概率,输入通道数,每个通道的独立样本数以及匹配的滤波后输出信噪比(S / N)功率比计算得出的。在此分析中,GLRT被证明比MLAD具有更好的检测性能。检测性能的差异随着使用较少的输入样本而增加。但是,对于两种检测方案,仅具有3 dB的检测损耗所必需的所需样本数量大致相同。这是很重要的,因为就目前而言,MLAD的实施要比GLRT复杂得多

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