基于逆匹配滤波的压缩感知SAR成像的研究

发布时间：2018-03-08 00:22

本文选题：合成　切入点：孔径雷达　出处：《中国科学技术大学》2017年硕士论文　论文类型：学位论文

【摘要】：合成孔径雷达利用小天线在平台上的运动来合成一个等效的长天线,在没有增加实际天线孔径的情况下提升了成像分辨率,对于传统的雷达成像来说是一个历史性的突破,使得合成孔径雷达成像无论在军用还是民用领域都得到了广泛应用。随着成像的目标场景越来越大,导致需要处理的数据量也随之增加,给硬件系统带来很大压力。而实际成像场景往往是稀疏的或具有某种结构性,因此可以用压缩感知理论进行处理。压缩感知理论利用信号中信息的冗余降低采样率,并证明当满足一定条件时可以利用稀疏优化算法从欠采样的数据中重构出原信号。和传统的信号采样理论相比,压缩感知算法将信号采样和压缩的步骤合并到一起,直接进行欠采样,减少了数据量,降低了数据存储和传输的压力。压缩感知SAR成像近年来吸引了众多学者的关注。本文研究了压缩感知SAR成像的重构算法,利用传统的匹配滤波方法对重构算法进行优化,降低重构算法复杂度。本文首先在绪论中介绍了合成孔径雷达的发展历史以及所面临的问题,接着引入了压缩感知理论,简要介绍了压缩感知SAR成像的理论和发展,在此基础上还介绍了单比特压缩感知以及单比特压缩感知SAR成像理论。本章提出了一些压缩感知SAR成像中仍然存在的问题,并将针对这些问题具体展开研究。第二章介绍了压缩感知理论以及稀疏重构算法。本章分析了压缩感知问题成立的条件,介绍了几种常用的压缩感知模型以及相应的重构算法,分析了不同重构算法的特点,并提出压缩感知SAR成像中存在的一些限制。第三章针对压缩感知SAR成像重构算法计算复杂度过高的问题提出了优化算法,将传统匹配滤波omega-K算法和压缩感知算法结合,提出了一种基于近似代替的低复杂度压缩感知SAR成像算法。本章证明了算法的可行性并进行了相应的理论推导,对时空复杂度进行了定量分析,利用匹配滤波降低了算法复杂度,减少了数据存储需求。实验结果验证了算法的有效性。第四章分析了上一章中算法在低信噪比情况下成像效果不好的问题,提出了一种低复杂度的单比特压缩感知SAR成像方法。不仅改善了算法在低信噪比情况下的重构性能,也缓解了接收端ADC的压力。在分析利用omega-K算法降低计算复杂度的可行性基础上,推导了具体计算过程。优化算法改善了低信噪比下的成像效果,降低了单比特压缩感知SAR成像算法的时空复杂度。实验结果验证了算法的有效性。
[Abstract]:Synthetic Aperture Radar (SAR) uses the motion of small antennas on the platform to synthesize an equivalent long antenna, which improves the imaging resolution without increasing the actual antenna aperture, which is a historic breakthrough for traditional radar imaging. Synthetic Aperture Radar (SAR) imaging has been widely used in both military and civil fields. As the target scene becomes larger and larger, the amount of data that needs to be processed increases. The actual imaging scene is often sparse or has some structure, so it can be processed by compression sensing theory, which reduces the sampling rate by using the redundancy of information in the signal. It is proved that the original signal can be reconstructed from the under-sampled data by sparse optimization algorithm when certain conditions are satisfied. Compared with the traditional signal sampling theory, the compression sensing algorithm combines the steps of signal sampling and compression together. Direct under-sampling reduces the amount of data and reduces the pressure of data storage and transmission. Compression sensing SAR imaging has attracted the attention of many scholars in recent years. In this paper, the reconstruction algorithm of compressed sensing SAR imaging is studied. The traditional matched filtering method is used to optimize the reconstruction algorithm to reduce the complexity of the reconstruction algorithm. Firstly, this paper introduces the history and problems of synthetic Aperture Radar (SAR) in the introduction, and then introduces the theory of compressed sensing. In this paper, the theory and development of compressed sensing SAR imaging are briefly introduced, and the theories of single bit compression sensing and single bit compression sensing SAR imaging are also introduced. In this chapter, some problems in compression sensing SAR imaging are presented. In chapter 2, the theory of compressed perception and sparse reconstruction algorithm are introduced. In this chapter, the conditions of the problem are analyzed, and several commonly used compression sensing models and corresponding reconstruction algorithms are introduced. The characteristics of different reconstruction algorithms are analyzed, and some limitations in compression sensing SAR imaging are proposed. In chapter 3, an optimization algorithm is proposed to solve the problem of high computational complexity of compression sensing SAR imaging reconstruction algorithms. Based on the combination of traditional matched filter omega-K algorithm and compression sensing algorithm, a low complexity compression sensing SAR imaging algorithm based on approximate substitution is proposed. The feasibility of the algorithm is proved in this chapter and the corresponding theoretical derivation is given. The complexity of time and space is analyzed quantitatively, and the algorithm complexity is reduced by using matched filter. The experimental results verify the effectiveness of the algorithm. Chapter 4th analyzes the problem of poor imaging performance in the case of low signal-to-noise ratio in the previous chapter. A low complexity single bit compression sensing SAR imaging method is proposed, which not only improves the reconstruction performance of the algorithm in the case of low signal-to-noise ratio (SNR). On the basis of analyzing the feasibility of using omega-K algorithm to reduce the computational complexity, the concrete calculation process is deduced. The optimized algorithm improves the imaging effect at low SNR. The time and space complexity of the single bit compression sensing SAR imaging algorithm is reduced. The experimental results show that the algorithm is effective.
【学位授予单位】：中国科学技术大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN957.52

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