Abstract
For improving the performance of intrusion discrimination in the dual Mach-zehnder interferometric (DMZI) perimeter system, we propose a novel method based upon local mean decomposition (LMD), independent component analysis (ICA) and features combination. By the LMD-ICA, the original signal is processed to construct a virtual noise, thereby obtaining the sensitive information of the signal. With multiple features from the sensitive information, the type of intrusions can be discriminated by the method of serial feature fusion (SFF). The experiments are performed with real data for the case of the single-vibration and the single-vibration under the rain interference. The results demonstrate that the proposed method is superior to the traditional discrimination one, with an average recognition rate of over 96%.
Highlights
As a sophisticated phase-modulation fiber sensing technique, dual Mach-zehnder interferometric (DMZI) system [1], [2] possesses the superiority of fast response and high sensitivity, and has been extensively employed in the realms of pipeline leakage detection [3], submarine cable security [4], airport guarding [5], et al For improving the efficiency of the DMZI system, several critical issues are proposed, involving endpoint detection [6], background noise removing [7], intrusion positioning [8], [9] as well as intrusion discrimination
For improving the performance of intrusion discrimination in the dual Mach-zehnder interferometric (DMZI) perimeter system, we propose a novel method based upon local mean decomposition (LMD), independent component analysis (ICA) and features combination
We propose a novel scheme of intrusion discrimination for the DMZI system, which is based on the LMD, ICA as well as serial feature fusion (SFF) [24]
Summary
As a sophisticated phase-modulation fiber sensing technique, dual Mach-zehnder interferometric (DMZI) system [1], [2] possesses the superiority of fast response and high sensitivity, and has been extensively employed in the realms of pipeline leakage detection [3], submarine cable security [4], airport guarding [5], et al For improving the efficiency of the DMZI system, several critical issues are proposed, involving endpoint detection [6], background noise removing [7], intrusion positioning [8], [9] as well as intrusion discrimination. Due to the diversity and complexity of invasion signals, feature description and classification for the intrusion signals are still immature. For some complex environmental conditions, such as in an airport, the existing methods of feature discrimination can not reach high efficiency and high classification rate. In recent years, a series of investigations are carried out for improving the performance of intrusion discrimination. In 2015, Liu [10] proposed a combined method of intrusion discrimination based upon empirical mode decomposition, kurtosis characteristics and a radial basis function neural network. This method improves the average recognition rate to above 87%, which can meet actual requirements initially. In 2017, Huang [11] designed a novel intrusion discriminations scheme in terms of hybrid feature extraction for the DMZI system, which can identify
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