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Subspace-Based Localization of Near-Field Signals in Unknown Nonuniform Noise

机译:基于子空间的近场信号本地化未知的非均匀噪声

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In this paper, we consider the problem of estimating the directions-of-arrival (DOAs) and ranges of multiple nearfield narrowband signals impinging on a symmetric uniform linear array (ULA) in nonuniform noise in practical applications. By forming a Toeplitz-like correlation matrix from the anti-diagonal elements of the array covariance matrix to convert the nonuniform noise to a uniform one, a new subspace-based localization method is proposed, where the null space is obtained through eigendecomposition of the resultant Toeplitz-like matrix, and the MUSIC method is used to estimate the location parameters, while a new pairing scheme is presented as well. Additionally, an oblique projector based alternating iteration is presented to improve the estimation accuracy of the location parameters, where the “saturation behavior” encountered in most of localization methods is solved effectively. Furthermore, the Cramér-Rao lower bound (CRB) for the near-field signals in unknown nonuniform noise is also derived explicitly. Finally, the effectiveness of the proposed method is verified through numerical examples.
机译:在本文中,我们考虑估计在实际应用中的非均匀噪声中施加对称均匀线性阵列(ULA)的到达方向(DOA)和范围的估计的近距离窄带信号的问题。通过从阵列协方差矩阵的抗对角线元件形成托对对角线的相关矩阵来将不均匀的噪声转换为统一的噪声,提出了一种新的基于子空间的本地化方法,其中通过结果分解获得空空格令人望远的矩阵,并且音乐方法用于估计位置参数,而还呈现了新的配对方案。另外,提出了一种基于倾斜投影仪的交流迭代以提高位置参数的估计精度,其中大多数本地化方法中遇到的“饱和行为”是有效的。此外,还明确地推导出未知的非均匀噪声近场信号的Cramér-Rao下限(CRB)。最后,通过数值例子验证了所提出的方法的有效性。

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