基于稀疏重建的大型阵列天线综合

发布时间：2018-08-25 09:46

【摘要】：大型阵列天线由于具有增益高、波瓣宽度窄、波束控制能力强等优点,在现代雷达及无线通信系统中得到了越来越广泛的研究与应用。同时,工程中为了降低成本和系统复杂度,一般希望将大型阵列设计成稀疏阵或子阵形式。然而,现有的设计方法在大型阵列应用中还面临着一些挑战。本文从实际应用需求出发,围绕大型阵列设计中的几个关键技术展开了研究,包括:稀疏阵列综合、基于方向图可重构天线的大间距阵和稀疏阵设计以及子阵合成,着重研究了稀疏重建在大型阵列天线综合中的应用。主要内容分以下五个部分:1.基于压缩感知理论的稀疏阵列综合方法从稀疏信号重建的角度审视了稀疏阵列综合问题,建立了基于压缩感知(CS)理论的稀疏阵列综合模型,将最大化稀疏阵列综合问题转化为线性约束下的稀疏重建问题。在此基础上,提出了一种基于欠定系统局域解法(FOCUSS)的综合方法,该方法可以智能地确定实现期望方向图所需的最小阵元个数、阵元的位置和激励,并且对稀疏线阵、面阵以及共形阵综合都适用。2.基于多测量向量协同稀疏重建的多方向图稀疏阵列综合方法提出了一种基于多测量向量欠定系统局域解法(M-FOCUSS)的多方向图稀疏阵列综合方法。该方法基于多测量向量协同稀疏重建(MMVCSR)理论,采用协同综合策略解决了传统方法在处理多方向图问题时出现的不同方向图的阵元位置分布不统一的问题。接着,将M-FOCUSS方法与有源方向图(AEP)技术合理结合,从而将实际阵列的互耦影响考虑到了稀疏阵列优化设计中。数值仿真和实物测试证明了该方法的有效性。3.基于扰动压缩采样的大型稀疏阵列快速综合方法首先,提出了一种基于扰动压缩采样(PCS)的稀疏阵列综合方法,该方法通过向传统的压缩感知理论模型中引入阵元位置扰动变量,建立了一种连续的阵元位置优化模型,扩展了问题的优化空间,同时减少了网格划分,大大提高了算法的优化效果和计算效率。接着,针对针对阵元位置扰动引起的近似误差问题,提出了一种扩展的扰动压缩采样(EPCS)方法,采用二级网格策略有效降低了标准PCS的建模误差和计算复杂度。然后,为解决PCS和EPCS算法在处理复值问题时阵元位置出现虚部的问题,提出了一种基于稀疏重建和局部优化的交替迭代算法,并用于复激励的多方向图稀疏面阵综合。最后,研究了PCS方法在可扫描大型稀疏阵列低副瓣综合中的应用,并对稀疏阵的阵元缩减比与扫描范围和阵列口径的关系进行了分析。4.基于方向图可重构天线的大间距阵和稀疏阵设计首先,提出了一种可实现二维圆极化波束切换的方向图可重构天线。然后,将方向图可重构天线分别用于矩形栅格大间距阵、三角栅格大间距阵以及大型随机稀疏阵设计中,实验结果表明,与传统阵列相比,基于此天线设计的阵列在阵元缩减比、波束扫描范围、扫描增益和副瓣电平性能上具有明显优势。5.均匀子阵的栅瓣抑制针对均匀子阵的栅瓣抑制问题,提出了一种基于波束指向不相同子阵模块和开关控制的栅瓣抑制方法,通过优化子阵方向图的波束指向,并在阵列扫描过程中有选择地激励部分子阵,使均匀子阵等效为一个子阵方向图各不相同的且激活子阵布局随扫描角变化的非均匀稀疏子阵。仿真结果表明,该方法可以实现对栅瓣电平的有效抑制,得到的子阵级阵列可保持与同口径阵元级相控阵相当的波瓣宽度,且尽管与阵元级相控阵相比,增益有所下降,但通道数的减少更加可观。
[Abstract]:Large array antennas have been widely studied and applied in modern radar and wireless communication systems because of their advantages such as high gain, narrow lobe width and strong beam control ability. At the same time, in order to reduce cost and system complexity, it is generally hoped to design large array into sparse array or sub-array. There are still some challenges in the application of the design method in large-scale arrays.In this paper, several key techniques in large-scale array design are studied, including sparse array synthesis, large-spacing array and sparse array design based on pattern reconfigurable antenna, and sub-array synthesis. The main contents are as follows: 1. Sparse array synthesis method based on compressed sensing theory examines sparse array synthesis problem from the point of sparse signal reconstruction, establishes sparse array synthesis model based on compressed sensing (CS) theory, transforms the problem of maximizing sparse array synthesis into one of maximizing sparse array synthesis. On this basis, a synthesis method based on under-determined system local solution (FOCUSS) is proposed. This method can intelligently determine the minimum number of elements needed to realize the desired pattern, the position and excitation of the elements, and is applicable to the synthesis of sparse linear, planar and conformal arrays. 2. Based on multiple measurements A new method of multi-directional pattern sparse array synthesis based on M-FOCUSS is proposed. This method is based on the theory of multi-vector cooperative sparse reconstruction (MMVCSR), and the traditional method is solved by the cooperative synthesis strategy. Then, the M-FOCUSS method is combined with the active pattern (AEP) technique reasonably, so that the mutual coupling effect of the actual array is taken into account in the sparse array optimization design. Numerical simulation and experimental results show that the method is effective. Firstly, a novel sparse array synthesis method based on perturbation compressed sampling (PCS) is proposed. By introducing the perturbation variables into the traditional compressed sensing model, a continuous optimization model of array position is established, which expands the optimization space of the problem and decreases the optimization space at the same time. Secondly, an extended perturbation compression sampling (EPCS) method is proposed to reduce the modeling error and computational complexity of standard PCS. Secondly, the modeling error and computational complexity of PCS and EPCS are reduced effectively to solve the approximate error caused by the position perturbation of array elements. An alternating iterative algorithm based on sparse reconstruction and local optimization is proposed for complex excitation sparse array synthesis. Finally, the application of PCS method in scannable large sparse array synthesis with low sidelobes is studied, and the sparse array elements are reduced. The relationship between the ratio and the scanning range and the array aperture is analyzed. 4. Based on the large spacing array and sparse array design of pattern reconfigurable antenna, a pattern reconfigurable antenna is proposed to realize two-dimensional circularly polarized beam switching. The experimental results show that the array based on this antenna has obvious advantages over traditional arrays in terms of element reduction ratio, beam scanning range, scanning gain and sidelobe level performance. 5. The grating lobe suppression of uniform subarray is proposed for the problem of grating lobe suppression of uniform subarray. By optimizing the beam direction of the subarray pattern and selectively exciting some subarrays in the scanning process of the array, the uniform subarray is equivalent to an inhomogeneous sparse subarray with different subarray pattern and different activation subarray layout varying with scanning angle. The real results show that this method can effectively suppress the grating lobe level, and the subarray array can maintain the same lobe width as the element-level phased array with the same aperture.
【学位授予单位】：电子科技大学
【学位级别】：博士
【学位授予年份】：2017
【分类号】：TN820

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