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Automatic detection of faults in optical fibers is an active area of researchthat plays a significant role in the design of reliable and stable opticalnetworks. A fiber measurement system that combines automated data acquisitionand processing represents a disruptive impact in the management of opticalfiber networks with fast and reliable event detection. It has been shown in theliterature that the linearized Bregman iterations (LBI) algorithm andvariations can be successfully used for processing and accurately identifyingfaults in a fiber profile. One of the factors that impact the performance ofthese algorithms is the degradation of spatial resolution, which is mainlycaused by the appearance of fault clusters due to a reduced number ofiterations. In this paper, a method is proposed based on an approximatedeconvolution approach for increasing the spatial resolution, possible after athorough analysis of fault clusters that appear in the algorithm's output. Theeffect of such approximate deconvolution is shown to extend beyond theimprovement of spatial resolution, allowing for better performances to bereached at shorter processing times. An efficient hardware architecture thatimplements the approximate deconvolution, compatible with the hardwarestructure recently presented for the LBI algorithm, is also proposed anddiscussed.

A Fiber Measurement System with Approximate Deconvolution Based on the Analysis of Fault Clusters in Linearized Bregman Iterations