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Improved structured least squares for the application of unitary ESPRIT to cross arrays

机译:改进的结构化最小二乘法,适用于将单一ESPRIT应用于交叉阵列

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A key problem in high-resolution multidimensional parameter estimation via unitary ESPRIT is to jointly solve a set of invariance equations by means of least-squares minimization. It has been shown previously that existing least-squares techniques fail when applied to the category of cross arrays, which consist of perpendicular uniform linear arrays crossing at the center of the array. Cross array geometries are of special interest because they provide a larger aperture and, hence, better resolution for a given number of array elements than other multidimensional uniform array geometries. This letter proposes an improved structured least-squares method that enables successful application of unitary ESPRIT to cross arrays. Results of simulated direction-of-arrival estimation experiments using a three-dimensional cross array indicate that considerable performance improvements can be achieved if the new method is used.
机译:通过单一ESPRIT进行高分辨率多维参数估计的关键问题是通过最小二乘最小化共同求解一组不变性方程。先前已经表明,将现有的最小二乘技术应用于交叉阵列的类别时会失败,该交叉阵列由在阵列中心交叉的垂直均匀线性阵列组成。交叉阵列几何形状特别受关注,因为与其他多维均匀阵列几何形状相比,它们为给定数量的阵列元素提供了更大的孔径并因此具有更好的分辨率。这封信提出了一种改进的结构化最小二乘法,该方法能够将统一的ESPRIT成功应用于交叉阵列。使用三维交叉阵列的模拟到达方向估计实验的结果表明,如果使用新方法,则可以实现相当大的性能改进。

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