Abstract We consider the problem of detecting distributed targets in Gaussian noise with unknown covariance matrix. To make the signal-plus-noise hypothesis more plausible in the mismatched case, we model the received signal under the signal-plus-noise hypothesis as the sum of noise, useful target echoes and fictitious signals. Two adaptive detectors are designed according to the Rao test and Wald test. We prove the proposed Rao test and Wald test exhibit constant false alarm rate properties against the covariance matrix. Numerical examples show that the proposed Rao test has strong robustness against the steering vector mismatches.

中文翻译：

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基于Rao检验和Wald检验的分布式目标鲁棒检测

摘要 我们考虑了在协方差矩阵未知的高斯噪声中检测分布式目标的问题。为了使信号加噪声假设在不匹配的情况下更合理，我们将信号加噪声假设下的接收信号建模为噪声、有用目标回波和虚构信号的总和。根据Rao测试和Wald测试设计了两个自适应检测器。我们证明了所提出的 Rao 检验和 Wald 检验对协方差矩阵表现出恒定的误报率特性。数值例子表明，所提出的 Rao 测试对导向矢量失配具有很强的鲁棒性。