|Title:||Signal processing techniques for direct-detected Φ-OTDR systems|
|Advisors:||Lu, Chao (EIE)|
|Subject:||Optical fiber detectors|
Hong Kong Polytechnic University -- Dissertations
|Department:||Department of Electronic and Information Engineering|
|Pages:||xxv, 161 pages : color illustrations|
|Abstract:||Direct detected Phase sensitive optical time-domain reflectometer (Φ-OTDR) has found applications in many research areas. Its applications in event detection and recognition, however, is limited by various system impairments. The thesis aims to study techniques to overcome these limitations. There exists problems with the contemporary solutions of mitigating Trace-to-trace fluctuation (TTF)-based noise effects. Moreover, a significantly low probability of significant data-traces in the differential data by a direct detected Φ- OTDR system is another challenge. Three characteristics of the Correlation-based match filtering (CMF) algorithm has been proved in Chapter (3) to mitigate the two above-mentioned limitations. This technique is better than the previously proposed techniques due to the cost-effectiveness, efficiency, and ease of adapting in a field environment. Three system performance limitations have been resolved concerning an explanation of three different applications, where each chapter is dedicated to explain each of these applications. Chapter (4) describes the usage of CMF algorithm in non-continuous activities for determining the frequency and duration of the applied impact which is only possible with the help of an efficient algorithm that enables the system to acquire a very less number of data-traces and by exploiting a few data-traces to differentiate an impact part of the data from that of non-impact part. Chapter (5) shed light on the possibility of improving SNR of the differential signals. It then explains the use of the proposed CMF algorithm to improve this SNR with the help of a lowest possible number of data traces. Chapter (6) elaborates some conditions for achieving a better classification accuracy in a multi-event perturbation recognition system. It also explains the effect of the CMF algorithm on improving the classification accuracy of the system. There are other issues in Φ-OTDR system which according to best of our knowledge have not been addressed till date. These issues are engendered if the length of a stretched fibre (p) is much greater than the spatial resolution (σ), a case which can normally be observed in a very long fibre under test (FUT) distributed sensing application as we can see in pipeline or border security-related field applications. In such an environment, it is most often difficult to determine the exact location of perturbation due to the propagation of the effect of perturbation across the length (p). Besides, with the same condition (σ << p) the spatially located zero energy points are created within the entire length (p). It is thus difficult to examine the exact non-zero energy points and it is hard to assess the useful data in time-domain if the zero energy points in spatial-domain are selected by the system. A dual space-time based window drag processing termed ST-scan method is adopted to determine the exact location of perturbation and the possibility of selecting zero energy points is reduced as the exact energy point has the fewer number of these energy points. The ST-scan method is comprised of different stages including an implementation of CMF algorithm for SNR improvement, selection of processing window to drag along the time-domain with optimum parameters, employing the use of maximum likelihood estimation (MLE) algorithm and the matrix-to-vector conversion by using an appropriate operator. All the processing stages in the newly proposed algorithm can be termed as the ST-scan method. The two mentioned problems (determination of the exact location of perturbation and a large probability of zero-energy points) are possible with the condition (σ << p ). Chapter (7) provide details about how the ST-scan method can be used to resolve these problems by using the data from a real-field environment with the FUT spread along a distance of 45km.|
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