Compression sampling algorithm of pipeline leak signal

The paper compares CS theory and traditional sampling theory and studies implementation of pipeline leak compression sampling based on CS theory, which includes: construction of the measurement matrix, selection of the sparse basis and signal-based matching pursuit reconstruction algorithm. A novel algorithm (Compressed Sensing, CS) for pipeline leak signal sampling and detection was proposed to resolve the problem of excessively high sampling rate of the pipeline leak signals. The algorithm is based on the sparse signals and the compressed sensing theory. It breaks through the limitation of the Shannon sampling theorem, and it implements Low-rate sampling lower than Nyquist sampling frequency, then finally realizes high precision reconstruction of the pipeline leak signal. In this paper, simulation experiment is performed for pipeline leak signal, performance indicator can be achieved by the compression sampling algorithm in conditions of different compression sampling ratio is given. The results indicate that the method proposed in this paper is correct and effective, can provide a new method for pipeline leak detection..