High spatial resolution radar using thinned arrays
High spatial resolution radar using thinned arrays
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DOI：
10.1109/radar.2017.7944372
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发表时间：
2017-05
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影响因子：
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通讯作者：
K. Mishra;Itay Kahane;A. Kaufmann;Yonina C. Eldar
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文献类型：
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作者：
K. Mishra;Itay Kahane;A. Kaufmann;Yonina C. Eldar
相控阵天线的结构和尺寸决定了雷达的波束方向图和空间分辨率。通常需要非常多的辐射单元来合成给定的阵列孔径，以便增强雷达明确区分近距离目标的能力。经典随机阵列理论为无空间混叠的大型阵列稀疏提供了工具，但代价是旁瓣电平高，性能不稳定。传统的波束形成和经典的数值方法(如MUSIC和ESPRIT)不能准确估计稀疏阵列的波达方向。本文提出了一种基于空间压缩感知的随机稀疏相控阵波达方向估计方法。我们还首次对相控阵-MIMO混合天线进行了稀疏处理，并对采用非重叠子阵的混合天线提出了较好的稀疏处理方法。我们使用最近提出的多分支匹配追踪(MBMP)算法来估计这些阵列的DOA。数值实验表明，MBMP算法的波达方向恢复误差比经典波束形成和正交匹配跟踪算法高出几个数量级。
The structure and size of a phased array antenna defines the beam pattern and the resultant spatial resolution of the radar. Often an exceedingly large number of radiating elements are required to synthesize a given array aperture in order to enhance the ability of the radar to unambiguously distinguish closely spaced targets. Classical random array theory provides tools for thinning of huge arrays without spatial aliasing, but at the cost of high sidelobe levels and unstable performance. Traditional beamforming and classical numerical methods (such as MUSIC and ESPRIT) cannot accurately estimate direction-of-arrival (DoA) for thinned arrays. In this paper, we employ a spatial compressed sensing (SCS) framework for accurate DoA estimation by a randomly thinned phased array. For the first time, we also sparsify a phased array-MIMO hybrid and suggest preferred thinning methods for the hybrid that uses non-overlapping subarrays. We estimate DoA for these arrays using the recently proposed multi-branch matching pursuit (MBMP) algorithm. Numerical experiments show MBMP outperforms classical beamforming and orthogonal matching pursuit by several orders of DoA recovery error.