吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (4): 1182-1190.doi: 10.13229/j.cnki.jdxbgxb201404043

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Weak periodic signal detection based on dissipative coupling synchronization and noise impact analysis

XING Hong-yan1, 2, 3, GONG Ping1, 2, 3, XU Wei1, 2, 3   

  1. 1.Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, University of Information Science and Technology, Nanjing 210044, China;
    2.Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, China;
    3.College of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • Received:2013-01-14 Online:2014-07-01 Published:2014-07-01

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Abstract: Based on the chaos forecasting model, a dissipative coupling detection method is proposed for detecting weak signal in chaotic background. The Radial Basis Function Neural Network (RBFNN) is applied to fit the chaos forecasting model. The synchronization between chaotic time series and chaos system is realized by combination of RBFNN and dissipative coupling. Then the synchronization error is used to detect the weak signal. The Rossler chaos system is taken as the object to test the feasibility of the proposed method and analyze its performance with weak signals of different frequencies. In order to avoid the mixed impact of noise and chaos time series to the performance of the method, the interference of noise intensity on weak signal detection is investigated in depth. Simulation results show that the proposed method can detect weak signal with different frequencies; under certain conditions, the method can detect weak periodic signal if the Signal-to-Clutter Ratio is bigger than -110 dB. The noise influence on the detection performance can be ignored if the Signal-to-Noise Ratio (SNR) ≥ 0 dB. On the basis of theoretical study, a new type chaotic evolution control experiment instrument MKS-CEC-Ⅲ is applied to produce practical Coullet chaotic time series and add weak signals with different frequencies into time series respectively. This method is used to detect the weak signal from the mixed signal. It is demonstrated that the method can detect added frequency signal from chaotic background and it can also be applied to other chaotic systems.

Key words: communication, chaos, dissipative coupling chaos synchronization, weak signal detection, radial basis function neural network

CLC Number: 

  • TN911.23
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