Xu Yanchun

Frequency detection of self-adaption control based on chaotic theory*

Low-order Duffing and high-order Rossler chaotic oscillator are connected together and new self-adaption frequency detection method is presented. The frequency difference control between unknown signal and the periodic driving force is realized in this paper and the self-adaption is obtained. Thus, the detection precision and speed are promoted. The limitation that there are too many chaotic oscillators in Duffing system is broken. Meanwhile the disadvantage that the detection speed is lower in Rossler chaotic control is overcome. The self-adaption choice of frequency difference control is realized using the Duffing and Rossler different chaotic oscillators to obtain unknown signal frequency. The simulation results show that the presented method is feasible and effective.

A new approach to detecting weak signal in strong noise based on chaos system control

In this paper, a chaos system and proportional differential control are both used to detect the frequency of an unknown signal. In traditional methods the useful signal is obtained through the Duffing equation or other chaotic oscillators. But these methods are too complex because of using a lot of chaos oscillators. In this paper a new method is presented that uses the Rossler equation and proportional differential control to detect a weak signal frequency. Substituting the detected signal frequency into the Rossler equation leads the Rossler phase state to be considerably changed. The chaos state can be controlled through the proportional differential method. Through its phase diagram and spectrum analysis, the unknown frequency is obtained. The simulation results verify that the presented method is feasible and that the detection accuracy is higher than those of other methods.

The study of low temperature target weak signal detection technique based on power spectrum

The low temperature target radiation energy has smaller absolute-value and the signal is weaker. Meanwhile there are a lot of the noises such as temperature noise, thermal noise and so on. It is very difficult to be detected precisely if the conventional method is used. In this paper according to autocorrelation and power spectrum being a couple of Fourier transforms theory, power spectrum is used to realize autocorrelation calculation so that low temperature target weak signal is detected. Using the method can get autocorrelation function through calculating the value of power spectrum and the unknown weak signal amplitude is detected. The specific algorithm is given in this paper and the device of low temperature target weak signal detection is designed through embedded system ARM. The experimental waveforms and measurement results all show that the method is feasible to detect low temperature target weak signal. The digital detection of weak photoelectric signal is realized through using power spectrum to calculate autocorrelation.