Weak Periodic Signal Detection Based On Chaos Theory

With the rapid development of science and technology, weak signal detection has been widely used in the fields of radar,fault diagnosis,system identification and communication,and it has always been the hot for domestic and foreign scholars. The extraction of weak signal under strong noise is a essential part of modern signal processing science. The commonly used frequency-domain and time-domain approaches in use nowadays has limitations, especially for weak signal detection under strong noise, the signal-noise ratio need further improvement. Because chaos oscillator is immune to noise and sensitive to weak signal, the detection method used in this paper is different from the traditional ones, overcoming the noise suppress problem.In order to effectively extract weak signal from noisy background, this paper improve the Duffing Chaos Oscillator model, and utilize chaos oscillator's characteristics, that the phase transition of it is extremely sensitive and immune to the interference signal with great noise and frequency different, to detect signal. Adjust the system parameter to detect signal with different frequency. Base on analysis of the chaos characteristics, band-pass filter and Gaussian characteristic, the mathematic model of Duffing oscillator for weak signal detecting is presented. By changing the magnitude of periodic perturbation, and observing image, the monoclinic orbit, furcation, chaos traction, critical periodic traction, large scale cycle of the system is derived. Construct a new weak periodic signal detection model by improving the nonlinear resilience term. Simulate using Matlab, and observe image changing. Results indicate that the advantage of Duffing oscillator method in weak periodic signal detection, and it is feasible for its frequency error rate is manageable.The research is an important means of developing high and new technologies, exploring and discovering new laws of nature. It has immediate applications in the detection of real signal , such as radar signal and underwater acoustic signal. And it has certain theoretical meaning and useful value in signal detecting and processing in the future.