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COMPARISON OF VARIOUS APPROACHES TO MULTI-CHANNEL
INFORMATION FUSION IN C-OTDR SYSTEMS FOR REMOTE MONITORING OF EXTENDED OBJECTS

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The paper presents new results concerning selection of optimal information fusion formula for ensembles of COTDR channels. Here C-OTDR is a coherent optical time domain reflectometer. Each of these channels provides data for appropriate automatic classifier which is designed to classify the elastic vibration sources in the multiclass case. Those classifiers form a so-called classifiers ensemble. Ensembles of Lipschitz Classifiers were considered. In this case the goal of information fusion is to create an integral classificator designed for effective classification of seismoacoustic target events. The Matching Pursuit Optimization Ensemble Classifiers (MPOEC), the Linear Programming Boosting (LP-Boost) (LP-β and LP-B variants), the Multiple Kernel Learning (MKL), and Weighing of Inversely as Lipschitz Constants (WILC) approaches were compared. The WILC is a brand new approach to optimal fusion of Lipschitz Classifiers Ensembles. The basics of these methods have been briefly described along with intrinsic features. All of those methods are based on reducing the task of choosing convex hull parameters to a solution of an optimization problem. All of the mentioned approaches can be successfully used for using in the C-OTDR system data processing. Results of practical usage are presented. 

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