用于分布式声传感的压缩域振动检测和分类
Compressed domain vibration detection and classification for distributed acoustic sensing
摘要：
分布式声学传感（DAS）是一种新的使能技术，可以将现有的光纤网络变成分布式声学传感器。然而，它面临着传输，存储和处理大量数据流的挑战，这些数据流的数量级大于从点传感器收集的数据。DAS产生的密集数据与读写速度和存储容量有限的现代计算系统之间的差距限制了许多应用。压缩感知（Compressive Sensing，CS）是一种革命性的信号采集方法，它可以用比Nyquist-Shannon定理所需的样本少得多的样本来采集和重构信号。虽然在采样阶段大大减少了数据量，但压缩数据的重建是耗时和计算量的。为了解决这一挑战，我们建议将特征提取器从奈奎斯特域映射到压缩域，因此可以直接在压缩域中实现振动检测和分类。测试结果表明，该框架可以将传输的数据量减少70%，同时在5 km传感光纤上实现99.4%的真阳性率（TPR）和0.04%的假阳性率（TPR），以及95.05%的分类准确率。
英文摘要：
Distributed acoustic sensing (DAS) is a novel enabling technology that can turn existing fibre optic networks to distributed acoustic sensors. However, it faces the challenges of transmitting, storing, and processing massive streams of data which are orders of magnitude larger than that collected from point sensors. The gap between intensive data generated by DAS and modern computing system with limited reading/writing speed and storage capacity imposes restrictions on many applications. Compressive sensing (CS) is a revolutionary signal acquisition method that allows a signal to be acquired and reconstructed with significantly fewer samples than that required by Nyquist-Shannon theorem. Though the data size is greatly reduced in the sampling stage, the reconstruction of the compressed data is however time and computation consuming. To address this challenge, we propose to map the feature extractor from Nyquist-domain to compressed-domain and therefore vibration detection and classification can be directly implemented in compressed-domain. The measured results show that our framework can be used to reduce the transmitted data size by 70% while achieves 99.4% true positive rate (TPR) and 0.04% false positive rate (TPR) along 5 km sensing fibre and 95.05% classification accuracy on a 5-class classification task.
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