Phase Dependent and Independent Frequency Identification of Weak Signals Based on Duffing Oscillator via Particle Swarm Optimization

In detecting weak signals based on the Duffing oscillator, it is usually assumed that the frequency is known, which is not always the case. This paper studies the problem of detecting the frequency of the to-be-detected weak signal based on the Duffing oscillator. For this purpose, the variance of t...

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Bibliographic Details
Published in:Circuits, systems, and signal processing systems, and signal processing, 2014, Vol.33 (1), p.223-239
Main Authors: Chang, Yuan, Hao, Yi, Li, Chunwen
Format: Article
Language:English
Subjects:
Algorithms
Circuits
Circuits and Systems
Computer simulation
Duffing oscillators
Electrical Engineering
Electronics and Microelectronics
Engineering
Frequencies
Instrumentation
Matlab
Optimization
Optimization techniques
Oscillators
Particle physics
Radio frequency identification
Signal processing
Signal,Image and Speech Processing
Swarm intelligence
Variance
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Summary:In detecting weak signals based on the Duffing oscillator, it is usually assumed that the frequency is known, which is not always the case. This paper studies the problem of detecting the frequency of the to-be-detected weak signal based on the Duffing oscillator. For this purpose, the variance of the Duffing oscillator’s output is exploited, which has the property of multi-extremum single-maximum (MESM) distribution with the frequency of the periodic signal. The impact of signal’s phase on the MESM distribution is discussed. When the signal’s phase is known, the frequency of the signal can be directly identified as that with the maximal variance, which leads to a nonlinear optimization problem that can be solved by a particle swarm optimization (PSO) algorithm. When the phase is unknown, the π /2-phase-shift method is to be exploited integrated with a PSO algorithm. It is shown that the frequency can be precisely and efficiently identified by this method, whose effectiveness is verified by simulation results in Matlab.
ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-013-9629-9