An Adaptive Compressed Sensing Algorithm of Optical Fiber Pipeline Pre-warning Data

For distributed optical fiber pipeline pre-warning system, the sampling rate used is very high and thus huge data will be generated, which makes it difficult to transfer and store. Compressive sensing is a new compressed sampling method in the field of signal processing which compresses and samples the signal simultaneously. In this paper, an adaptive compressive sensing method is presented for compression and reconstruction of distributed optical fiber pipeline data. First, partial reconstruction based detection method is used to detect whether a hazardous event happened, then different compression ratios are taken for different classes of signal thereby increasing the compression ratio. In signal reconstruction phase, a sparsity determination algorithm is used to determine the sparsity of different segment of the signal, and then wavelet tree combined with CoSamp algorithm is adopted to reconstruct the signal. The adaptive compression algorithm improves the compression ratio and the sparsity determination in reconstruction phase can determine the sparsity of each segment when the signal varies without prior knowledge of the sparsity of the signal. Experimental results show that, the proposed algorithm can obtain higher reconstruction accuracy at a relatively high compression ratio. Furthermore, location simulation shows that the reconstructed signal by the proposed method is effective for danger signal positioning.