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Abstract and Applied Analysis
Volume 2013, Article ID 420605, 10 pages
Research Article

Study of the Method of Multi-Frequency Signal Detection Based on the Adaptive Stochastic Resonance

1College of Electrical and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
2Jiangsu Meteorological Observatory, Nanjing 210008, China
3College of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, China

Received 1 January 2013; Accepted 12 March 2013

Academic Editor: Xuerong Mao

Copyright © 2013 Zhenyu Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Recently, the stochastic resonance effect has been widely used by the method of discovering and extracting weak periodic signals from strong noise through the stochastic resonance effect. The detection of the single-frequency weak signals by using stochastic resonance effect is widely used. However, the detection methods of the multifrequency weak signals need to be researched. According to the different frequency input signals of a given system, this paper puts forward a detection method of multifrequency signal by using adaptive stochastic resonance, which analyzed the frequency characteristics and the parallel number of the input signals, adjusted system parameters automatically to the low frequency signals in the fixed step size, and then measured the stochastic resonance phenomenon based on the frequency of the periodic signals to select the most appropriate indicators in the middle or high frequency. Finally, the optimized system parameters are founded and the frequency of the given signals is extracted in the frequency domain of the stochastic resonance output signals. Compared with the traditional detection methods, the method in this paper not only improves the work efficiency but also makes it more accurate by using the color noise, the frequency is more accurate being extracted from the measured signal. The consistency between the simulation results and analysis shows that this method is effective and feasible.