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BLIND MULTI-TARGET DETECTION FOR BISTATIC MIMO RADAR BASED ON RANDOM MATRIX THEORY

机译:基于随机矩阵理论的基于随机矩阵雷达的盲多目标检测

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In this paper, the random matrix theory (RMT) is applied in detecting the number of targets in the large dimensional regime for bistatic multiple input multiple output (MIMO) radar systems. A blind MIMO radar multi-target detection algorithm is proposed exploiting a sequence of nested RMT-based generalized-likelihood ratio test (GLRT) hypothesis tests. The decision threshold is derived by use of the Marcenko-Pastur (M-P) law and Tracy-Widom distribution. The proposed algorithm does not require the priori information of noise variance, target scattering and location. Therefore, it has advantages in the presence of noise uncertainty. Simulations are conducted to illustrate the effectiveness of the proposed algorithm. Compared with two conventional algorithms of minimum description length (MDL) and Akaike information criteria (AIC), it owns superiorities in both performance at lower SNR and consistency at larger snapshot size.
机译:在本文中,应用随机矩阵理论(RMT)检测对BISTOG多维输入多输出(MIMO)雷达系统的大维度的目标数量。提出了一种盲目MIMO雷达多目标检测算法利用嵌套的基于RMT的广义似然比测试(GLRT)假设试验序列。决策阈值是通过使用Marcenko-Pastur(M-P)法律和特雷西普遍分布来源的。该算法不需要噪声方差,目标散射和位置的先验信息。因此,它在存在噪声不确定性方面具有优势。进行仿真以说明所提出的算法的有效性。与最小描述长度(MDL)和Akaike信息标准(AIC)的两个传统算法相比,它在较低的SNR下具有较低的SNR的性能和较大快照大小的一致性。

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