We consider the weak target detection problem with unknown parameter in colocated multiple-input multiple-output (MIMO) radar. To cope with the sheer amount of data for large-size systems, a multi-bit quantizer is utilized in the sampling process. As a low-complexity alternative to classic generalized likelihood ratio test (GLRT) for quantized data, we propose the multi-bit detector on Rao test with a closed-form test statistic, whose theoretical asymptotic distribution is provided to generalize the actual detection performance. Besides, we refine the design of quantizer by optimized quantization thresholds, which are obtained resorting to the popular particle swarm optimization algorithmthe (PSOA). The simulation is conducted to demonstrate the performance variations of detectors based on unquantized and quantized data. The numerical results corroborate our theoretical analyses and show that the performance with 3-bit quantization approaches the case without quantization.

翻译：我们考虑的是位于同一地点的多投入多产出(MIMO)雷达中未知参数的薄弱目标探测问题。 为了应对大型系统的数据数量之大,在取样过程中使用了多位量的量化器。作为典型通用概率比测试(GLRT)的低复杂度替代品,我们建议用封闭式测试统计数据来取代典型通用概率比测试(GLRT),在Rao测试上使用多位量检测器,该测试器的理论性能分布将普遍化实际检测性能。此外,我们通过优化量化阈值来改进量化器的设计,该阈值将使用流行的粒子温优化算法(PSOA)获得。进行模拟是为了展示基于未量化和量化数据的探测器性能变化。数字结果证实了我们的理论分析,并表明3位四分法的性能在没有量化的情况下接近案件。