Abstract
In this paper, an improved propagator method (PM) is proposed by using a two-parallel array consisting of two uniform large-spacing linear arrays. Because of the increase of element spacing, the mutual coupling between two sensors can be reduced. Firstly, two matrices containing elevation angle information are obtained by PM. Then, by performing EVD of the product of the two matrices, the elevation angles of incident signals can be estimated without direction ambiguity. At last, the matrix product is used again to obtain the estimations of azimuth angles. Compared with the existed PM algorithms based on conventional uniform two-parallel linear array, the proposed PM algorithm based on the large-spacing linear arrays has higher estimation precision. Many simulation experiments are presented to verify the effect of proposed scheme in reducing the mutual coupling and improving estimation precision.
Highlights
Estimating the directions of arrival (DOA) of spatial signals by sensors array has widespread application in wireless communication [1] and multiple input multiple output (MIMO) radar [2]
L-shaped array and parallel array are frequently used array constructions for 2D DOA estimation. e L-shaped array consists of two orthogonal linear arrays, based on which many effective 2D DOA estimation algorithms [3, 4] were proposed
In [11], propagator method (PM) algorithm was used with the parallel factor analysis (PARAFAC) model to estimate the 2D DOA, where the PARAFAC model is solved by circulative iteration
Summary
Estimating the directions of arrival (DOA) of spatial signals by sensors array has widespread application in wireless communication [1] and multiple input multiple output (MIMO) radar [2]. E L-shaped array consists of two orthogonal linear arrays, based on which many effective 2D DOA estimation algorithms [3, 4] were proposed. In [10], another modified 2D PM algorithm based on three-parallel linear array was proposed for reducing computational complexity. International Journal of Antennas and Propagation array aperture and reduce mutual coupling, some nonuniform sparse array constructions such as nested array [14,15,16,17] and coprime array [18,19,20] were proposed. For the sparse arrays [14,15,16,17,18,19], the interval of adjacent sensors can exceed half a wavelength of the incident signal which can reduce the mutual coupling. Compared with the PM [8,9,10], the proposed algorithm has two obvious advantages: (1) the proposed algorithm can reduce the mutual coupling considerably due to the use of large-spacing linear array; (2) the proposed algorithm has higher estimation precision than PM [8,9,10], even if the mutual coupling between sensors is ignored
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