A new detection method based on CFAR and DE for OFPS

Abstract

Optical fiber pre-warning system (OFPS) is widely utilized in pipeline transport fields. The intrusions of OFPS need to be located. In this system, the original signals consist of noises, interferences, and intrusion signals. Here, noises are background and harmless interferences possessing with high power, and the intrusion signals are the main target of detection in this system. Hence, the study stresses on extracting the intrusion signals from the total ones. The proposed method can be divided into two parts, constant false alarm rate (CFAR) and dilation and erosion (DE). The former is applied to eliminate noises, and the latter is to remove interferences. According to some researches, the feature of noise background accords with the CFAR spatial detection. Furthermore, the detection results after CFAR can be presented as a binary image of time and space. Besides, interferences are relatively disconnected. Consequently, they can be eliminated by DE which is introduced from the image processing. To sum up, this novel method is based on CFAR and DE which can eliminate noises and interferences effectively. Moreover, it performs a brilliant detection performance. A series of tests were developed in Men Tou Gou of Beijing, China, and the reliability of proposed method can be verified by these tests.

Mellin Transform-Based Correction Method for Linear Scale Inconsistency of Intrusion Events Identification in OFPS

“…The waveform comparison between the selected template signals is shown in Fig. 4, where 1 L is the length of the template, and 2 L is the length of signals. It can be seen from Fig.…”

“…5. After this, the template and signals have the same length, 1 L . It illustrates that signals which belong to the same type can be effectively converted to the same scale by the scale correction method.…”

“…With the increasing demand of security in the fields of borders and oil pipelines, the optical fiber pre-warning system (OFPS) has attracted growing attention [1][2][3]. The optical fiber sensor has many advantages, such as anti-electromagnetic interference, good electrical insulation, passive sensors, high sensitivity, wide measuring range, and strong adaptability to various environmental conditions [4,5].…”

Ensemble Learning with Stochastic Configuration Network for Noisy Optical Fiber Vibration Signal Recognition

“…When we choose OFPS based on Φ-OTDR to recognize fiber vibration signals, how to improve the recognition rate of different vibration signals and noisy vibration signals is a challenging task. On the one hand, some scholars studied from the aspect of traditional feature extraction [7,8,9,10,11,12,13,14], on the other hand, some scholars studied from the aspect of combining neural network [15,16], and all of them achieved certain results.…”

RVFL-Based Optical Fiber Intrusion Signal Recognition With Multi-Level Wavelet Decomposition as Feature

“…Some documents proposed they found an effective way to set the soft or hard thresholds for every point along the fiber adaptively to improve the signal-to-noise ratio (SNR) [7,8]. With different background noises, the constant false alarm rate (CFAR) algorithm is exploited to achieve an optimal adaptive threshold detection [9,10].…”