本文選題：寬帶線性調(diào)頻信號(hào)　切入點(diǎn)：信號(hào)抑噪　出處：《電子科技大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：寬帶線性調(diào)頻信號(hào)在軍事、雷達(dá)及聲吶等范圍內(nèi)被普遍應(yīng)用。如何對(duì)雷達(dá)回波信息進(jìn)行噪聲抑制已成為信號(hào)處理領(lǐng)域中的研究熱點(diǎn)。盡管在以前的幾十年里,關(guān)于信號(hào)的噪聲抑制理論和算法得到了一定的發(fā)展,然而大部分方法對(duì)寬帶線性調(diào)頻信號(hào)抑噪?yún)s已不再適用。本文主要針對(duì)寬帶線性調(diào)頻信號(hào)的特征及處理方法,從信號(hào)噪聲抑制方法的基礎(chǔ)上展開深入研究。從提高輸出信噪比、降低算法復(fù)雜度入手,研究不同類型的噪聲對(duì)寬帶線性調(diào)頻信號(hào)的影響,以及不同抑噪算法對(duì)寬帶線性調(diào)頻信號(hào)噪聲抑制的效果。同時(shí),并通過大量仿真驗(yàn)證了本文所提及抑噪方法的優(yōu)勢(shì)和有用性。本文研究的重點(diǎn)內(nèi)容和創(chuàng)新點(diǎn)如下:首先,研究分析不同噪聲對(duì)寬帶線性調(diào)頻信號(hào)的影響。雷達(dá)的距離分辨率可以采用非常窄的脈沖來顯著地提高,而采用較窄的脈沖會(huì)降低平均發(fā)射功率,因?yàn)樗c接收機(jī)信噪比之間的關(guān)系很緊密,所以通常期望在增加脈寬的同時(shí)保持足夠的分辨率。使用脈沖壓縮方法將會(huì)使這種期望成為可能,脈沖壓縮雷達(dá)的抗干擾能力很強(qiáng),而普通噪聲對(duì)其干擾效果有限。因而,本文先研究不同噪聲干擾對(duì)寬帶線性調(diào)頻信號(hào)的影響,仿真結(jié)果表明各種噪聲干擾效果不一。其次,研究基于小波變換的信號(hào)抑噪方法,從基于多分辨分析概念產(chǎn)生的小波分解與重構(gòu)抑噪,到從小波奇異性檢測(cè)理論而產(chǎn)生的小波變換模極大值抑噪,及小波變換閾值抑噪;同時(shí)闡述了基于稀疏分解的信號(hào)抑噪理論,并研究分析了貪婪匹配追蹤算法,同時(shí)結(jié)合小波抑噪,進(jìn)一步研究基于稀疏的抑噪方法。仿真實(shí)驗(yàn)結(jié)果表明:針對(duì)寬帶線性調(diào)頻信號(hào)噪聲的抑制,在抑噪效果方面,稀疏分解總體優(yōu)于小波變換;而在運(yùn)算速度方面,小波明顯比稀疏分解占有優(yōu)勢(shì)。最后,深入研究了基于經(jīng)驗(yàn)?zāi)B(tài)分解的三種抑噪算法,針對(duì)原模態(tài)相關(guān)法未考慮到噪聲分量中可能會(huì)含有有用信號(hào)的問題,給出了新模態(tài)相關(guān)小波抑噪算法,仿真實(shí)驗(yàn)顯示新算法的抑噪效果明顯比原算法具有優(yōu)勢(shì)。然后將小波變換與稀疏表示相結(jié)合,給出了改進(jìn)后的基于經(jīng)驗(yàn)?zāi)B(tài)分解的抑噪算法,仿真結(jié)果驗(yàn)證了該方法的有效性,信噪比并得到了一定的提高。
[Abstract]:Wideband linear frequency modulation (LFM) signals are widely used in military, radar and sonar fields. How to suppress the noise of radar echo information has become a research hotspot in the field of signal processing. The theory and algorithm of signal noise suppression have been developed, but most of the methods are no longer applicable to wideband LFM signals. This paper focuses on the characteristics and processing methods of wideband LFM signals. Based on the method of signal noise suppression, the effect of different types of noise on wideband LFM signal is studied by improving the output SNR and reducing the complexity of the algorithm. And the effect of different noise suppression algorithms on the noise suppression of wideband LFM signals. At the same time, the advantages and usefulness of the noise suppression methods mentioned in this paper are verified by a large number of simulations. The key contents and innovations of this paper are as follows: first, The influence of different noise on wideband LFM signal is studied. The range resolution of radar can be improved significantly by using very narrow pulse, but the average transmitting power can be reduced by using narrower pulse. Because it is closely related to the signal-to-noise ratio (SNR) of the receiver, it is usually expected to maintain sufficient resolution while increasing the pulse width. This expectation will be made possible by using the pulse compression method, and the anti-jamming capability of the pulse compression radar is very strong. The effect of ordinary noise on the interference is limited. Therefore, the influence of different noise on wideband LFM signal is studied in this paper, and the simulation results show that the noise jamming effect is different. Secondly, the signal noise suppression method based on wavelet transform is studied. From wavelet decomposition and reconstruction based on the concept of Multiresolution analysis to wavelet transform modulus maximum noise suppression and wavelet transform threshold noise suppression; At the same time, the theory of signal noise suppression based on sparse decomposition is expounded, and the greedy matching tracking algorithm is studied and analyzed. The simulation results show that for wideband LFM signal noise suppression, the sparse decomposition is better than wavelet transform in noise suppression effect, but in the aspect of computing speed, Wavelet is obviously superior to sparse decomposition. Finally, three noise suppression algorithms based on empirical mode decomposition are studied in depth. A new mode-dependent wavelet denoising algorithm is presented. The simulation results show that the new algorithm has obvious advantages over the original algorithm. Then, an improved denoising algorithm based on empirical mode decomposition is proposed by combining wavelet transform with sparse representation. The simulation results show that the method is effective and SNR is improved.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN911.4

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 李燕平;汪志強(qiáng);肖yN;;LFM信號(hào)的卷積調(diào)制干擾仿真[J];電子信息對(duì)抗技術(shù);2011年04期

2 郭雪鋒;方立軍;馬駿;張焱;;寬帶線性調(diào)頻信號(hào)的性能檢測(cè)方法[J];雷達(dá)科學(xué)與技術(shù);2012年05期

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本文編號(hào)：1592751