Target classification identification method used for distributed optical fiber sensing system

Abstract

The invention discloses a target classification identification method used for a distributed optical fiber sensing system, and relates to the technical field of target identification. The method mainly comprises the processing steps of data screening, feature extraction, classifier design, cross testing, actual application and the like. According to the method, multiple feature parameters of target data are subjected to maximum likelihood ratio detection method-based multilevel classification through a binary detection classifier, and information fusion processing is performed in time and space dimensions, so that high-efficiency and low-false-alarm-rate target detection and classification identification are realized; and the method has the characteristics of high target detection identification probability, low false alarm rate, high calculation speed and simple engineering realization, solves the problem of high false alarm rate of sensing detection in a complex working environment or a complex target detection identification algorithm, and is especially suitable for the target detection identification process under the conditions of long-distance detection, complex and diversified environments and the like.

Description

technical field [0001] The invention relates to the technical field of target recognition, in particular to a target classification and recognition method with a low false alarm rate for a distributed optical fiber sensing system. Background technique [0002] (Optical Time Domain Reflectometer, Optical Time Domain Reflectometer) Distributed optical fiber sensing system can be used to sense and detect active targets in long-distance areas where cables are laid. However, due to the long cable laying distance and the complex and diverse environment in long-distance areas, the detection false alarm rate is high. In addition, since the distributed sensing system needs to process the target data in real time, the processing time resources are limited, so the complex target detection and classification methods cannot be adopted. [0003] Currently, Distributed optical fiber sensing systems generally adopt time-domain processing methods with relatively simple calculations such...

AI Technical Summary

Problems solved by technology

However, due to the long cabling distance and the complex and diverse environment in long-distance areas, the detection false alarm rate is high

In addition, since the distributed sensing system needs to process the target data in real time, the processing time resources are limited, so the complex target detection and classification methods cannot be adopted.

[0003] Currently, Distributed optical fiber sensing systems generally use time-domain processing methods with relatively simple calculations such as amplitude judgment. This type of method has a fast processing speed but a high false alarm rate, which seriously affects the performance of this type of sensing system.

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