基于结构化压缩感知方法的管道检测应用研究

根据压缩感知(CS)理论及管道泄漏信号特征，提出管道泄漏信号结构化测量矩阵部分重构(SRMPR)的压缩采样和检测定位方法。该方法以远低于Nyquist采样率对管道泄漏信号同步实现压缩采样，并在部分重构过程中实现泄漏检测定位。与传统相关定位法的仿真实验比较的结果表明，当信号长度为4096时，SRMPR方法比传统相关定位方法精确度提高0.34%；当压缩采样比为5%时，重构信噪比达到30.44dB。本文所提方法能够重构管道泄漏信号重要特征，具有可行性和有效性，可以满足管道泄漏信号实时检测定位的需求。

Pipeline leak detection based on the structurally random matrix compressed sensing method

According to compressed sensing (CS) theory and the character of pipeline leak signals, this paper proposes a structurally random matrix partial reconstruction (SRMPR) method based on compressed sensing for compression sampling, detection and localization of pipeline leak signals. It achieves compressive sampling for pipeline leak signals with sampling ratios much lower than those of the traditional Nyquist theory. It also allows complete detection and localization in the process of partial reconstruction. For a 4096 point signal, the experimental results show that the location accuracy of the SRMPR method is 0.34% higher than that of the traditional cross correlation method. At a compression ratio of 5%, the value of the signal to noise ratio is as high as 30.44dB, which allows important features of pipeline leak signals to be reconstructed. The results show that the method is a feasible and effective way to ensure accurate real time detection and localization of pipeline leak signals.