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

机译:未知非均匀噪声中基于子空间的近场信号定位

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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,我们考虑了在实际应用中估计非均匀噪声中撞击在对称均匀线性阵列(ULA)上的多个近场窄带信号的到达方向(DOA)和范围的问题。通过从阵列协方差矩阵的反对角元素形成一个Toeplitz型相关矩阵,将非均匀噪声转换为均匀噪声,提出了一种新的基于子空间的定位方法,该方法通过特征分解得到零空间。类Toeplitz矩阵和MUSIC方法用于估计位置参数,同时还提出了一种新的配对方案。此外,提出了一种基于倾斜投影仪的交替迭代方法,以提高位置参数的估计精度,从而有效解决了大多数定位方法中遇到的“饱和行为”。此外,还明确导出了未知非均匀噪声下近场信号的Cramér-Rao下界(CRB)。最后,通过数值算例验证了所提方法的有效性。

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