基于混沌和小波变换的信号检测方法  

Signal Detection Based on Wavelet Transform and Chaotic Theory

作　　者：邓宏贵[1] 曹文晖[1] 高小龙[1] 敖邦乾[1] 

机构地区：[1]中南大学物理科学与技术学院,湖南长沙410083

出　　处：《控制工程》2011年第6期937-940,946,共5页Control Engineering of China

基　　金：国家自然科学基金(607710287);中南大学学位论文创新资助项目(2010ssxt012)

摘　　要：针对微弱周期信号提出小波阈值去噪和混沌系统相结合的微弱周期信号检测新方法,该方法利用小波变换的平滑作用对包含噪声的信号进行有限离散处理,并根据小波自适应分解尺度确定阈值去噪深度,然后根据混沌系统对噪声的免疫性和对周期信号的敏感性,把重构的信号作为周期策动力的摄动并入混沌系统,由混沌系统完成微弱信号检测;并改进了Duffing方程,可用于不同频率信号的检测,同时使混沌系统的相轨迹由临界状态变为大尺度周期运动更灵敏。测试结果表明,本方法克服了以往小波分解对尺度确定的盲目性和阈值选择的不合理性;同时通过调节混沌系统频率对周期信号的敏感性提高了信号的检测精确度,其检测的信噪比下限达到-64.7 dB;完全适用于毫伏级下信号的检测应用,证明了提出的方法是合理和有效的。The weak signal detection method is presented based on the combination of the wavelet threshold de-noising and; chaotic sys- tem. The wavelet smoothing effect is used to make the limite discrete processing of the signal with noise. The scale of the wavelet adaptive decomposition is used to determine the de-nosing depth. Then the weak periodical signal is detected by substituting the reconstruct signal as the driving motivation of the perturbation cycle into the chaotic system. The proposed method improves the Duffing equation to adapt to the different frequency signals and makes the chaotic system phase trajectory from the critical state to the large-scale periodic motion becoming more sensitive. Simulation results show that the method can overcome the blindness of the determination of scale and the irrationality choice of the threshold of the traditional wavelet decomposition method, and improve the detection accuracy by adjusting the frequency of chaotic systems. The SNR is extended to -65.7dB, which apply to the millivolt-level signal detection, and the pro- posed method is reasonable and effective.

关 键 词：混沌 小波阈值去噪 微弱信号 信噪比 信号检测 

分 类 号：TP27[自动化与计算机技术—检测技术与自动化装置]