微弱信号混沌检测系统的噪声分离研究

Noise Separation From the Weak Signal Detection Chaotic System

混沌学是目前非线性科学研究中的热点之一.传统的微弱信号混沌检测技术在信号存在噪声的情况下暴露出许多不足之处,如去噪能力较差、检测精度不高等,本文基于前人的研究基础,提出了一种改进小波变换算法的微弱信号混沌检测系统的方法,通过仿真实验可知能够将该方法运用到微弱信号检测.具体方法是对传统小波变换算法的变换域变量进行离散化,目的是消除变换中的冗余,之后采用阈值折衷策略对小波系数进行阈值优化,处理后的小波算法将应用于微弱信号混沌检测系统中,周期策动力为有限离散处理后的含噪信号并入混沌系统,从而实现含噪情况下的微弱信号检测.一系列仿真实验表明,提出和改进的小波变换算法的去噪效果要优于传统小波变换算法,同时在微弱信号混沌检测系统的应用中,改进算法的检测精度和鲁棒性更好.

The traditional weak signal chaos detection system has some technique problems when the signal is with noise,such as poor denoising ability and low detection precision. This paper proposes a novel weak signal chaos detection system based on an improved wavelet transform algorithm. First,the traditional wavelet transform algorithm domain variables are transformed and discretized to eliminate the redundant transform. Then,based on the discrete optimization,the wavelet coefficients are optimized by threshold compromise strategy. The improved wavelet transform algorithm is applied in the weak signal chaos detection system. The noise signal after finite discrete processing is treated as a perturbation of cycle power and put into a chaotic system for detecting weak signal under the noise conditions. The simulation experiments show that the proposed improved wavelet transform algorithm has a better denoising effect than traditional wavelet transform algorithm. Moreover,the improved algorithm shows better accuracy and higher robustness in the weak signal chaos detection system.