For a passive direction of arrival (DoA) measurement system using massive multiple input multiple output (MIMO), it is mandatory to infer whether the emitter exists or not before performing DOA estimation operation. Inspired by the detection idea from radio detection and ranging (radar), three high-performance detectors are proposed to infer the existence of single passive emitter from the eigen-space of sample covariance matrix of receive signal vector. The test statistic (TS) of the first method is defined as the ratio of maximum eigen-value (Max-EV) to minimum eigen-value (R-MaxEV-MinEV) while that of the second one is defined as the ratio of Max-EV to noise variance (R-MaxEV-NV). The TS of the third method is the mean of maximum eigen-value (EV) and minimum EV(M-MaxEV-MinEV). Their closed-form expressions are presented and the corresponding detection performance is given. Simulation results show that the proposed M-MaxEV-MinEV and R-MaxEV-NV methods can approximately achieve the same detection performance that is better than the traditional generalized likelihood ratio test method with false alarm probability being less than 0.3.

中文翻译：

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使用大规模 MIMO 接收器的单发射器高性能无源特征模型检测器

对于使用大规模多输入多输出 (MIMO) 的无源到达方向 (DoA) 测量系统，必须在执行 DOA 估计操作之前推断发射器是否存在。受无线电检测和测距（雷达）检测思想的启发，提出了三种高性能检测器，用于从接收信号向量样本协方差矩阵的特征空间推断单个无源发射器的存在。第一种方法的检验统计量（TS）定义为最大特征值（Max-EV）与最小特征值（R-MaxEV-MinEV）之比，而第二种方法定义为最大特征值（Max-EV）与最小特征值（R-MaxEV-MinEV）之比-EV 到噪声方差 (R-MaxEV-NV)。第三种方法的 TS 是最大特征值 (EV) 和最小 EV(M-MaxEV-MinEV) 的平均值。给出了它们的闭式表达式并给出了相应的检测性能。仿真结果表明，所提出的 M-MaxEV-MinEV 和 R-MaxEV-NV 方法可以达到近似相同的检测性能，优于传统的广义似然比测试方法，误报概率小于 0.3。