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Research Article

RVM Based Compressed Sensing for Optical Fiber Pipeline Data Compression

HongJie Wan, Xuegang Chen, HaoJiang Deng and Xiang Cui

Due to long time continuous operation and the high sampling rate, the distributed optical fiber pre-warning system will generate vast amounts of data. Compressed sensing can sample and compress the signal at the same time thus reduce the data amount to be stored or transferred. Therefore, this article uses compressed sensing approach to compress the optical fiber pre-warning data. In compressed sampling phase, the signal is classified using sparse detection method, then more measurements will be taken for the segment containing threatening event than the segment of normal operation, through this the amount of data can be further reduced. In the signal reconstruction phase, the signal compressed sampling process is modeled by the relevant vector machine and the signal recovery is implemented by probabilistic parameter estimation methods. Using Bayesian framework, the sparsity of the signal and the noise each is modeled by a prior. The sparsity and the noise can be estimated in parameter estimation process and the sparsity needn't be given in advance. The optical fiber pre-warning system is long-running system and the sparsity of the signal will change with time, so the automatic sparsity determination ability is superior to other existing recovery methods. Experimental results show that, under the same measurement, the proposed method can reconstruct the signal with high quality and the reconstructed signal will not affect the positioning result.

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  How to cite this article:

HongJie Wan, Xuegang Chen, HaoJiang Deng and Xiang Cui, 2013. RVM Based Compressed Sensing for Optical Fiber Pipeline Data Compression. Journal of Applied Sciences, 13: 1877-1882.

DOI: 10.3923/jas.2013.1877.1882


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