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Vibration discrimination based upon multifractal spectrum and improved probabilistic neural network in the dual Mach–Zehnder interferometric perimeter system


For improving the performance of vibration discrimination in terms of dual Mach–Zehnder interferometric (DMZI) perimeter system, we proposed a novel method based upon the multifractal theory, serial feature fusion as well as improved probabilistic neural network (PNN). By the multifractal theory, the features of original signal are extracted in the form of multifractal spectrum parameters, thereby constructing the feature vector by the method of serial feature fusion (SFF). Then, we employ the simulated annealing algorithm to automatically optimize the smoothing factor of PNN, which can avoid manual selection of empirical thresholds in the process of feature extraction and pattern recognition. Finally, with the simulated annealing-based PNN, the intrusion signal of DMZI system can be discriminated and classified. Compared with traditional methods based upon signal decomposition, our method focuses on the morphological characteristics of original signal, therefore possesses a better ability of detail discrimination. In the DMZI perimeter system, four types of real vibration intrusions are completed to verify the proposed method. The results demonstrate that it is superior to the conventional methods, with an average discrimination rate of over 95%.

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The authors acknowledge the financial support by “National Key R&D Program of China (2020YFB1600101 and 2020YFB1600103)”.

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Correspondence to Meng Li.

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Li, M., Li, J., Xiong, X. et al. Vibration discrimination based upon multifractal spectrum and improved probabilistic neural network in the dual Mach–Zehnder interferometric perimeter system. Opt Rev 29, 13–24 (2022).

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  • Vibration discrimination
  • Multifractal spectrum
  • Improved probabilistic neural network
  • Serial feature fusion
  • Dual Mach–Zehnder interferometric perimeter system