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Polarization Difference Smoothing for Direction Finding of Coherent Signals

机译:用于相干信号测向的极化差平滑

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摘要

The subspace-based two-dimensional direction finding with an array of electromagnetic vector sensors for multiple coherent signals amid unknown noise fields is investigated. The noise can be spatially nonuniform and/or correlated. A novel preprocessing method, called polarization difference smoothing (PDS), is proposed. With PDS the unknown noise is removed by using the difference of pairs of electromagnetic component's data correlation matrices, and the coherent signals are decorrelated by summing these difference correlation matrices. Unlike most other existing preprocessing techniques, such as spatial smoothing and forward-backward averaging, PDS processing does not decrease the array aperture and is applicable to arbitrary array geometry. As an example a uniform rectangular array is considered, and a computationally-efficient propagator-based algorithm (PDS-propagator) is derived. Monte Carlo simulations demonstrate that, with an appropriately chosen PDS matrix, the PDS-based eigenstructure algorithms can offer better performance than the polarization smoothing-based (PS) counterparts. Incidently in the presence of external interference noise, the PDS-based algorithms can underperform the PS-based algorithms.
机译:研究了在未知噪声场中使用电磁矢量传感器阵列对多个相干信号进行基于子空间的二维测向。噪声在空间上可能是不均匀的和/或相关的。提出了一种新的预处理方法,称为极化差平滑(PDS)。对于PDS,通过使用成对的电磁分量数据相关矩阵的差消除未知噪声,并通过对这些差相关矩阵求和来对相干信号进行解相关。与大多数其他现有的预处理技术(例如空间平滑和前后平均)不同,PDS处理不会减小阵列孔径,并且适用于任意阵列几何形状。作为示例,考虑统一的矩形阵列,并得出计算效率高的基于传播器的算法(PDS-传播器)。蒙特卡洛模拟表明,通过适当选择PDS矩阵,基于PDS的特征结构算法可以提供比基于偏振平滑(PS)的同类算法更好的性能。偶然地,在存在外部干扰噪声的情况下,基于PDS的算法可能不如基于PS的算法。

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