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.

翻译：对于使用大规模多重输入多重输出(MIMO)的被动抵达方向测量系统(DoA),在进行DOA估计操作之前,必须推断排放物是否存在,在无线电探测和测距(radar)的探测想法的启发下,建议采用三种高性能探测器,以推断从接收信号矢量的样本共变异矩阵的eigen-空间的单一被动排放物的存在。第一个方法的测试统计(TS)被定义为最大eigen值(Max-EV)与最小eigen值(R-Max-MaxEV-Minev)的比率,而第二个方法的第二个方法被定义为Max-EVE与噪音差异(R-Max-MaxEV-NV)的比率。第三个方法的TS是最大eigen值(EV)和最小 EV(M-Max-MaxEV-MinevEV)的平均值。其封闭式表象表和相应的检测性能。模拟结果显示,拟议的M-Max-MaxEVEV和R-Max-VINVV的数值与0.30的概率率比常规测算法的概率要低。