Adaptive Stochastic Resonance Weak Signal Detection

Authors

Hongyan Xing, 1.Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China; ;2.Key Laboratory ofMeteorologicalObservation and Information Processingof Jiangsu Province , Nanjing University of Information Science & Technology, Nanjing 210044, China;
Chunxia Lu, 1.Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China; ;2.Key Laboratory ofMeteorologicalObservation and Information Processingof Jiangsu Province , Nanjing University of Information Science & Technology, Nanjing 210044, China;
Zhang Qiang, 1.Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China; ;2.Key Laboratory ofMeteorologicalObservation and Information Processingof Jiangsu Province , Nanjing University of Information Science & Technology, Nanjing 210044, China;

Abstract

Abstract: As traditional stochastic resonance methodhas low accuracy problem for weak signal detection,a new adaptive stochastic resonanceweak signal detection method based on global artificial fish swarm algorithm (GAFSA) is proposed, in which the global artificial fish swarm algorithm is combined with stochastic resonance. The output signal-to-noise ratio gain of stochastic resonance system is used as fitness function of the global artificial fish swarm algorithm, and the weak signal detection problem is transformed into multi-parameter parallel optimization problem, which formsan adaptive system to detect weak feature signal. The simulation experiments and the comparisons of optimization results are carried out respectively in the Langevin and Duffing systems. Simulation results show thatthe proposed method is efficient and feasible. Compared to Langevin system, Duffing adaptive stochastic resonance system has higher detection accuracy and performance of the weak signal detection. Duffing optimal system is applied to the multi-frequency large signal detection, which widens the application range of stochastic resonance.

Recommended Citation

Xing, Hongyan; Lu, Chunxia; and Qiang, Zhang (2019) "Adaptive Stochastic Resonance Weak Signal Detection," Journal of System Simulation: Vol. 30: Iss. 2, Article 27.
DOI: 10.16182/j.issn1004731x.joss.201802027
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss2/27

First Page

587

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201802027

Last Page

594

CLC

N945.15;TP273

Recommended Citation

Xing Hongyan, Lu Chunxia, Zhang Qiang. Adaptive Stochastic Resonance Weak Signal Detection[J]. Journal of System Simulation, 2018, 30(2): 587-594.

DOI

10.16182/j.issn1004731x.joss.201802027