本文選題：混沌信號(hào) + 小波變換�。� 參考：《太原理工大學(xué)》2014年碩士論文

【摘要】：近年來,由確定非線性系統(tǒng)產(chǎn)生的混沌信號(hào)已經(jīng)廣泛應(yīng)用于通信、信號(hào)檢測(cè)等領(lǐng)域�；煦缧盘�(hào)在其產(chǎn)生、傳輸過程中,不可避免地被噪聲污染,嚴(yán)重時(shí)甚至?xí)蜎]數(shù)據(jù)的內(nèi)在本質(zhì),使對(duì)數(shù)據(jù)所做出的分析和預(yù)測(cè)偏離實(shí)際需要,并給后期的數(shù)據(jù)分析和研究帶來了誤差。然而混沌信號(hào)與噪聲的宏觀統(tǒng)計(jì)特性又表現(xiàn)的驚人的相似。因此能夠準(zhǔn)確的辨別混沌信號(hào)和噪聲,抑制和消除噪聲,提高系統(tǒng)的信噪比已經(jīng)成為對(duì)混沌現(xiàn)象研究與應(yīng)用中的一個(gè)重要方面并且受到廣泛的關(guān)注。 本文對(duì)被噪聲“污染”的高頻Colpitts混沌信號(hào)進(jìn)行降噪研究,提出了提升小波與粒子群相結(jié)合的降噪方法,并將該方法應(yīng)用到混沌雷達(dá)測(cè)距旁瓣抑制中。本文主要做的工作有：(1)從小波形式和閾值兩個(gè)參數(shù)的選取兩方面出發(fā),探討了小波變換能夠應(yīng)用于被噪聲“污染”的高頻Colpitts混沌信號(hào)降噪。(2)針對(duì)小波變換在小波形式和閾值選取時(shí)的限制,提出了提升小波與粒子群算法相結(jié)合的降噪方法,并分別對(duì)含有高斯白噪聲和高斯有色噪聲的混沌信號(hào)進(jìn)行仿真分析。該方法解決了實(shí)際應(yīng)用中硬閾值降噪法中閾值函數(shù)不連續(xù)的和軟閾值降噪法中存在恒定偏差的問題,不僅提高了混沌系統(tǒng)本身的信噪比(SNR),還降低了均方根誤差。(3)從混沌雷達(dá)測(cè)距的原理出發(fā),用小波閾值降噪法和提升小波變換與粒子群相結(jié)合的降噪法分別對(duì)雷達(dá)采集到的回波信號(hào)進(jìn)行降噪處理,從峰值旁瓣比和積分旁瓣比兩個(gè)參數(shù)來衡量降噪效果,證明了能夠抑制混沌雷達(dá)測(cè)距中因噪聲產(chǎn)生的旁瓣,從而達(dá)到提高雷達(dá)精度的目的。 通過仿真分析可知,提升小波與粒子群相結(jié)合的降噪法在理論上能夠?qū)Ρ辉肼暋拔廴尽钡幕煦缧盘?hào)進(jìn)行有效降噪；還通過對(duì)混沌雷達(dá)測(cè)距實(shí)驗(yàn)數(shù)據(jù)進(jìn)行降噪處理,證明了該方法具有一定的實(shí)際應(yīng)用價(jià)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.

【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.7;O174.2

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