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Electromagnetic Vector Sparse Nested Array: Array Structure Design, Off-Grid Parameter Estimation Algorithm

机译:电磁矢量稀疏嵌套阵列:阵列结构设计,离网参数估计算法

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In this paper, a new array structure of sparse nested array (SNA) for electromagnetic vector sensor is designed. An electromagnetic vector sensor is composed of six spatially colocated, orthogonally oriented, diversely polarized antennas, which can measure three-dimensional electric and magnetic field components. By introducing sparse factor (SF) between every adjacent sensor, the proposed SNA has flexibility of extending the array aperture and reducing the mutual coupling effect. Meanwhile, a low-complexity multiparameter estimation algorithm is proposed for SNA. First, the vectorization operation for array manifold ensures the large degrees of freedom for multiparameter estimation, where the initial coarse estimates decrease search range. In addition, the improved off-grid orthogonal matching pursuit method obtains joint direction of arrival (DOA) and polarization estimates with a relatively small overcomplete dictionary because this off-grid method achieves high performance even if the estimates do not fall on the grid of the dictionary. Theoretical analysis and simulation results verify the superiority of the proposed array structure and the algorithm.
机译:在本文中,设计了一种用于电磁矢量传感器的稀疏嵌套阵列(SNA)的新阵列结构。电磁矢量传感器由六个空间光源的,正交定向的多样化天线组成,其可以测量三维电磁场部件。通过在每个相邻传感器之间引入稀疏因子(SF),所提出的SNA具有延伸阵列孔径并降低相互耦合效果的灵活性。同时,提出了一种低复杂性多游艇仪估计算法,用于SNA。首先,对于阵列歧管中的矢量化操作确保大自由度的多参数估计,其中所述初始粗估计减小搜索范围。此外,改进的离网正交匹配追踪方法获取到达的关节方向(DOA)和极化估计具有相对小的过完备字典因为这个离网方法实现高性能即使估计不落在的网格字典。理论分析和仿真结果验证了所提出的阵列结构和算法的优越性。

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