基于稀疏重建的大型陣列天線綜合
發(fā)布時間：2018-08-25 09:46
【摘要】：大型陣列天線由于具有增益高、波瓣寬度窄、波束控制能力強等優(yōu)點,在現代雷達及無線通信系統(tǒng)中得到了越來越廣泛的研究與應用。同時,工程中為了降低成本和系統(tǒng)復雜度,一般希望將大型陣列設計成稀疏陣或子陣形式。然而,現有的設計方法在大型陣列應用中還面臨著一些挑戰(zhàn)。本文從實際應用需求出發(fā),圍繞大型陣列設計中的幾個關鍵技術展開了研究,包括:稀疏陣列綜合、基于方向圖可重構天線的大間距陣和稀疏陣設計以及子陣合成,著重研究了稀疏重建在大型陣列天線綜合中的應用。主要內容分以下五個部分:1.基于壓縮感知理論的稀疏陣列綜合方法從稀疏信號重建的角度審視了稀疏陣列綜合問題,建立了基于壓縮感知(CS)理論的稀疏陣列綜合模型,將最大化稀疏陣列綜合問題轉化為線性約束下的稀疏重建問題。在此基礎上,提出了一種基于欠定系統(tǒng)局域解法(FOCUSS)的綜合方法,該方法可以智能地確定實現期望方向圖所需的最小陣元個數、陣元的位置和激勵,并且對稀疏線陣、面陣以及共形陣綜合都適用。2.基于多測量向量協同稀疏重建的多方向圖稀疏陣列綜合方法提出了一種基于多測量向量欠定系統(tǒng)局域解法(M-FOCUSS)的多方向圖稀疏陣列綜合方法。該方法基于多測量向量協同稀疏重建(MMVCSR)理論,采用協同綜合策略解決了傳統(tǒng)方法在處理多方向圖問題時出現的不同方向圖的陣元位置分布不統(tǒng)一的問題。接著,將M-FOCUSS方法與有源方向圖(AEP)技術合理結合,從而將實際陣列的互耦影響考慮到了稀疏陣列優(yōu)化設計中。數值仿真和實物測試證明了該方法的有效性。3.基于擾動壓縮采樣的大型稀疏陣列快速綜合方法首先,提出了一種基于擾動壓縮采樣(PCS)的稀疏陣列綜合方法,該方法通過向傳統(tǒng)的壓縮感知理論模型中引入陣元位置擾動變量,建立了一種連續(xù)的陣元位置優(yōu)化模型,擴展了問題的優(yōu)化空間,同時減少了網格劃分,大大提高了算法的優(yōu)化效果和計算效率。接著,針對針對陣元位置擾動引起的近似誤差問題,提出了一種擴展的擾動壓縮采樣(EPCS)方法,采用二級網格策略有效降低了標準PCS的建模誤差和計算復雜度。然后,為解決PCS和EPCS算法在處理復值問題時陣元位置出現虛部的問題,提出了一種基于稀疏重建和局部優(yōu)化的交替迭代算法,并用于復激勵的多方向圖稀疏面陣綜合。最后,研究了PCS方法在可掃描大型稀疏陣列低副瓣綜合中的應用,并對稀疏陣的陣元縮減比與掃描范圍和陣列口徑的關系進行了分析。4.基于方向圖可重構天線的大間距陣和稀疏陣設計首先,提出了一種可實現二維圓極化波束切換的方向圖可重構天線。然后,將方向圖可重構天線分別用于矩形柵格大間距陣、三角柵格大間距陣以及大型隨機稀疏陣設計中,實驗結果表明,與傳統(tǒng)陣列相比,基于此天線設計的陣列在陣元縮減比、波束掃描范圍、掃描增益和副瓣電平性能上具有明顯優(yōu)勢。5.均勻子陣的柵瓣抑制針對均勻子陣的柵瓣抑制問題,提出了一種基于波束指向不相同子陣模塊和開關控制的柵瓣抑制方法,通過優(yōu)化子陣方向圖的波束指向,并在陣列掃描過程中有選擇地激勵部分子陣,使均勻子陣等效為一個子陣方向圖各不相同的且激活子陣布局隨掃描角變化的非均勻稀疏子陣。仿真結果表明,該方法可以實現對柵瓣電平的有效抑制,得到的子陣級陣列可保持與同口徑陣元級相控陣相當的波瓣寬度,且盡管與陣元級相控陣相比,增益有所下降,但通道數的減少更加可觀。
[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.
【學位授予單位】：電子科技大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：TN820
本文編號：2202504
[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.
【學位授予單位】：電子科技大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：TN820
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