小波变换在混沌信号降噪中的应用

发布时间：2018-04-21 20:23

本文选题：混沌信号 + 小波变换　；参考：《太原理工大学》2014年硕士论文

【摘要】：近年来,由确定非线性系统产生的混沌信号已经广泛应用于通信、信号检测等领域。混沌信号在其产生、传输过程中,不可避免地被噪声污染,严重时甚至会淹没数据的内在本质,使对数据所做出的分析和预测偏离实际需要,并给后期的数据分析和研究带来了误差。然而混沌信号与噪声的宏观统计特性又表现的惊人的相似。因此能够准确的辨别混沌信号和噪声,抑制和消除噪声,提高系统的信噪比已经成为对混沌现象研究与应用中的一个重要方面并且受到广泛的关注。本文对被噪声“污染”的高频Colpitts混沌信号进行降噪研究,提出了提升小波与粒子群相结合的降噪方法,并将该方法应用到混沌雷达测距旁瓣抑制中。本文主要做的工作有：(1)从小波形式和阈值两个参数的选取两方面出发,探讨了小波变换能够应用于被噪声“污染”的高频Colpitts混沌信号降噪。(2)针对小波变换在小波形式和阈值选取时的限制,提出了提升小波与粒子群算法相结合的降噪方法,并分别对含有高斯白噪声和高斯有色噪声的混沌信号进行仿真分析。该方法解决了实际应用中硬阈值降噪法中阈值函数不连续的和软阈值降噪法中存在恒定偏差的问题,不仅提高了混沌系统本身的信噪比(SNR),还降低了均方根误差。(3)从混沌雷达测距的原理出发,用小波阈值降噪法和提升小波变换与粒子群相结合的降噪法分别对雷达采集到的回波信号进行降噪处理,从峰值旁瓣比和积分旁瓣比两个参数来衡量降噪效果,证明了能够抑制混沌雷达测距中因噪声产生的旁瓣,从而达到提高雷达精度的目的。通过仿真分析可知,提升小波与粒子群相结合的降噪法在理论上能够对被噪声“污染”的混沌信号进行有效降噪；还通过对混沌雷达测距实验数据进行降噪处理,证明了该方法具有一定的实际应用价值。
[Abstract]:In recent years, chaotic signals produced by nonlinear systems have been widely used in the fields of communication, signal detection and so on. Chaotic signals are inevitably polluted by noise during their production and transmission, and even inundate the intrinsic nature of the data, so that the analysis and prediction of data are deviated from the actual needs, and the number of later periods is given. According to the analysis and research, the error is brought about. However, the macroscopic statistical characteristics of the chaotic signal and the noise are remarkably similar. Therefore, it is an important aspect in the study and application of chaos phenomenon that it can accurately distinguish the chaotic signal and noise, suppress and eliminate the noise and improve the signal to noise ratio of the system.
In this paper, the noise reduction of high frequency Colpitts chaotic signal is studied. The noise reduction method combined with the lifting wavelet and particle swarm is proposed, and the method is applied to the chaotic radar ranging sidelobe suppression. The main work of this paper is as follows: (1) from the two aspects of the selection of two parameters of the wavelet form and the threshold. The wavelet transform can be applied to noise reduction of high frequency Colpitts chaotic signal which is polluted by noise. (2) in view of the limitation of wavelet transform in the selection of wavelet form and threshold, a noise reduction method combining lifting wavelet with particle swarm optimization is proposed, and the simulation analysis of chaotic signals with Gauss white noise and Gauss colored noise are simulated respectively. The method solves the problem of constant deviation in threshold function discontinuous and soft threshold denoising in hard threshold denoising, which not only improves the signal to noise ratio (SNR) of the chaotic system itself, but also reduces the root mean square error. (3) based on the principle of chaotic radar ranging, the wavelet threshold denoising method and the lifting wavelet transform are used. The method of particle swarm optimization is used to denoise the echo signal collected by radar respectively. The noise reduction effect is measured from the two parameters of the peak side lobe ratio and the integral sidelobe ratio. It is proved that the sidelobe produced by the noise in the range of chaotic radar is suppressed, thus the purpose of improving the radar precision is to be achieved.
The simulation analysis shows that the noise reduction method which combines the lifting wavelet with the particle swarm can effectively reduce the noise of the chaotic signal contaminated by the noise in theory, and the noise reduction processing of the experimental data of the chaotic radar range finder proves that the method has some practical value.

【学位授予单位】：太原理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN911.7;O174.2

【参考文献】