宽带线性调频信号噪声抑制技术研究

发布时间：2018-03-10 09:32

本文选题：宽带线性调频信号　切入点：信号抑噪　出处：《电子科技大学》2015年硕士论文　论文类型：学位论文

【摘要】：宽带线性调频信号在军事、雷达及声呐等范围内被普遍应用。如何对雷达回波信息进行噪声抑制已成为信号处理领域中的研究热点。尽管在以前的几十年里,关于信号的噪声抑制理论和算法得到了一定的发展,然而大部分方法对宽带线性调频信号抑噪却已不再适用。本文主要针对宽带线性调频信号的特征及处理方法,从信号噪声抑制方法的基础上展开深入研究。从提高输出信噪比、降低算法复杂度入手,研究不同类型的噪声对宽带线性调频信号的影响,以及不同抑噪算法对宽带线性调频信号噪声抑制的效果。同时,并通过大量仿真验证了本文所提及抑噪方法的优势和有用性。本文研究的重点内容和创新点如下:首先,研究分析不同噪声对宽带线性调频信号的影响。雷达的距离分辨率可以采用非常窄的脉冲来显著地提高,而采用较窄的脉冲会降低平均发射功率,因为它与接收机信噪比之间的关系很紧密,所以通常期望在增加脉宽的同时保持足够的分辨率。使用脉冲压缩方法将会使这种期望成为可能,脉冲压缩雷达的抗干扰能力很强,而普通噪声对其干扰效果有限。因而,本文先研究不同噪声干扰对宽带线性调频信号的影响,仿真结果表明各种噪声干扰效果不一。其次,研究基于小波变换的信号抑噪方法,从基于多分辨分析概念产生的小波分解与重构抑噪,到从小波奇异性检测理论而产生的小波变换模极大值抑噪,及小波变换阈值抑噪;同时阐述了基于稀疏分解的信号抑噪理论,并研究分析了贪婪匹配追踪算法,同时结合小波抑噪,进一步研究基于稀疏的抑噪方法。仿真实验结果表明:针对宽带线性调频信号噪声的抑制,在抑噪效果方面,稀疏分解总体优于小波变换;而在运算速度方面,小波明显比稀疏分解占有优势。最后,深入研究了基于经验模态分解的三种抑噪算法,针对原模态相关法未考虑到噪声分量中可能会含有有用信号的问题,给出了新模态相关小波抑噪算法,仿真实验显示新算法的抑噪效果明显比原算法具有优势。然后将小波变换与稀疏表示相结合,给出了改进后的基于经验模态分解的抑噪算法,仿真结果验证了该方法的有效性,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 The theory and algorithm of signal noise suppression have been developed, but most of the methods are no longer applicable to wideband LFM signals.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.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TN911.4

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