改进Omega-K算法以及运动补偿研究

发布时间：2018-05-16 09:04

本文选题：Omega-K算法 + 改进参考函数相乘　；参考：《西安电子科技大学》2014年硕士论文

【摘要】：合成孔径雷达(synthetic aperture radar,SAR)具有远作用距离、全天时、全天候和宽测绘带等优点,并且可以实现对地面热点地区长时间观测,因此在军事和民用领域,SAR都取得了广泛应用。对于SAR数据处理而言,成像操作以及运动补偿数据处理是关键步骤,并且一直是研究的重点和难点。传统的时域成像操作虽然也能产生较好的图像聚焦效果,但运算量较大,操作运行效率较低。频域处理算法如距离多普勒(RD)算法和其改进的CS算法虽然解决了运行效率问题,但对于大斜视和宽孔径成像的处理仍然比较困难。本文致力于大斜视构型的SAR成像以及运动补偿的研究,首先介绍了SAR成像和运动补偿的背景和研究现状,然后研究了SAR成像的基本理论和常用的成像处理及运动补偿处理算法,主要内容包括以下两个方面:1.提出了一种改进的Omega-K算法用于大斜视构型成像问题。大斜视构型下,回波信号二维频谱的支撑区会发生严重倾斜,从而造成了stolt插值困难。针对该问题,提出了一种适用于大斜视构型的改进的二维频谱求解方法。首先应用驻定相位原理推导了传统的Omega-K算法的频谱,并应用大斜视构型的距离等式推导了大斜视构型下的改进频谱。然后基于得到的改进二维频谱,提出大斜视改进的参考函数相乘和和改进的stolt插值。最后针对正侧视、中等斜视构型和大斜视构型分别进行了点仿真分析,并对改进后的算法和传统的Omega-K算法以及RD算法进行了聚焦效果的比较。2.提出了一种基于切比雪夫逼近和级数反演的搜索优化PGA算法用于运动补偿问题。该算法对传统的PGA算法的相位梯度估计进行了改进。首先根据加权理论对各个距离单元基于其能量进行了加权处理,并得到相位梯度中心值。然后提出了相位梯度步长值的概念,将图像熵映射为关于相位梯度步长值的函数,并利用切比雪夫不等式来逼近图像熵值,然后根据级数反演法得到该步长值。由相位梯度中心值和步长值得到更加精确的相位误差,对该相位误差进行补偿。该搜索优化处理减少了传统的PGA算法的迭代次数,而且对传统PGA算法处理效果较差甚至不能进行运动补偿处理的有极少孤立强散射点的图像有良好的补偿效果。最后针对具有大量孤立强散射点的图像图像和低对比度图像分别进行了仿真分析,并对搜索优化PGA算法和传统的PGA算法进行了迭代次数和相位误差补偿效果的比较。本文工作得到了国家自然科学基金(No.61173092)、新世纪优秀人才支持计划(No.66ZY110)和陕西省科学技术研究发展计划项目(No.2013KJXX-64)资助。
[Abstract]:Synthetic aperture radar (SAR) has the advantages of long range, all-weather, all-weather and wide mapping band, and can be used for long time observation of hot spot areas on the ground, so it has been widely used in military and civil fields. For SAR data processing, imaging operation and motion compensation data processing are the key steps, and have always been the focus and difficulty of the research. Although the traditional time-domain imaging operation can also produce better image focusing effect, it has a large amount of computation and low operational efficiency. Although the frequency domain processing algorithm such as range Doppler RD algorithm and its improved CS algorithm solve the problem of running efficiency, it is still difficult to deal with large squint and wide aperture imaging. This paper is devoted to the research of SAR imaging and motion compensation of large squint configuration. Firstly, the background and research status of SAR imaging and motion compensation are introduced, then the basic theory of SAR imaging and the common algorithms of imaging processing and motion compensation processing are studied. The main content includes the following two aspects: 1. An improved Omega-K algorithm is proposed for large squint configuration imaging. In the large squint configuration, the support region of the two-dimensional spectrum of the echo signal will be skewed seriously, which makes the stolt interpolation difficult. In order to solve this problem, an improved two-dimensional spectrum solution method for large squint configuration is proposed. Firstly, the spectrum of the traditional Omega-K algorithm is derived by using the stationary phase principle, and the improved spectrum under the large squint configuration is derived by using the distance equation of the large squint configuration. Then, based on the improved two-dimensional spectrum, the improved large squint reference function multiplication and improved stolt interpolation are proposed. Finally, point simulation analysis is carried out for the positive side view, medium squint configuration and large squint configuration, and the focus effect of the improved algorithm is compared with that of the traditional Omega-K algorithm and Rd algorithm. 2. A search optimization PGA algorithm based on Chebyshev approximation and series inversion is proposed for motion compensation. This algorithm improves the phase gradient estimation of the traditional PGA algorithm. Firstly, each distance element is weighted based on its energy according to the weighting theory, and the center value of phase gradient is obtained. Then the concept of phase gradient step size is proposed and the image entropy is mapped to a function about the phase gradient step size. The image entropy value is approximated by Chebyshev inequality and the step size value is obtained according to the series inversion method. The phase error is compensated by the phase gradient center value and step size. The search optimization process reduces the number of iterations of the traditional PGA algorithm and has a good compensation effect for images with few isolated and strong scattering points which have poor processing effect or even cannot be processed by the traditional PGA algorithm. Finally, the simulation and analysis of the images with a large number of isolated and strong scattering points and low contrast images are carried out, and the iterative times and phase error compensation effects of the search optimization PGA algorithm and the traditional PGA algorithm are compared. This work is supported by the National Natural Science Foundation No. 61173092, the New Century Talent support Program No. 66ZY110) and the Shaanxi Provincial Science and Technology Research and Development Program Project No. 2013KJXX-64).
【学位授予单位】：西安电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN957.52

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