论文标题
用于检测水管道通道中泄漏信号的假设测试程序
Hypothesis Test Procedures for Detecting Leakage Signals in Water Pipeline Channels
论文作者
论文摘要
我们设计了用于在水管线通道中执行泄漏检测的统计假设检验。通过应用适当的模型进行信号传播，我们表明检测问题成为将信号与噪声区分开的一个，其中噪声由具有未知协方差矩阵的多元高斯分布描述。我们首先设计了基于广义似然比测试的测试程序，我们通过模拟显示，该测试比在类似环境（用于雷达检测）中设计的常规方法提供了可观的泄漏检测性能增益。我们提出的方法需要估算噪声协方差矩阵，在高维度下以及稀缺的测量数据时，该方法可能会变得不准确。为了解决这个问题，我们提出了第二种泄漏检测方法，该方法采用了正则协方差矩阵估计。通过应用大尺寸随机矩阵理论的结果，为泄漏检测应用进行了优化的正则化参数。与第一种方法相比，这第二种提出的方法显示出可提高泄漏检测的性能，但要牺牲更高的计算复杂性。
We design statistical hypothesis tests for performing leak detection in water pipeline channels. By applying an appropriate model for signal propagation, we show that the detection problem becomes one of distinguishing signal from noise, with the noise being described by a multivariate Gaussian distribution with unknown covariance matrix. We first design a test procedure based on the generalized likelihood ratio test, which we show through simulations to offer appreciable leak detection performance gain over conventional approaches designed in an analogous context (for radar detection). Our proposed method requires estimation of the noise covariance matrix, which can become inaccurate under high-dimensional settings, and when the measurement data is scarce. To deal with this, we present a second leak detection method, which employs a regularized covariance matrix estimate. The regularization parameter is optimized for the leak detection application by applying results from large dimensional random matrix theory. This second proposed approach is shown to yield improved performance in leak detection compared with the first approach, at the expense of requiring higher computational complexity.
