Abstract
Electromagnetic vector sensor (EVS) array has drawn extensive attention in the past decades, since it offers two-dimensional direction-of-arrival (2D-DOA) estimation and additional polarization information of the incoming source. Most of the existing works concerning EVS array are focused on parameter estimation with special array architecture, e.g., uniform manifold and sparse arrays. In this paper, we consider a more general scenario that EVS array is distributed in an arbitrary geometry, and a novel estimator is proposed. Firstly, the covariance tensor model is established, which can make full use of the multidimensional structure of the array measurement. Then, the higher-order singular value decomposition (HOSVD) is adopted to obtain a more accurate signal subspace. Thereafter, a novel rotation invariant relation is exploited to construct a normalized Poynting vector, and the vector cross-product technique is utilized to estimate the 2D-DOA. Based on the previous obtained 2D-DOA, the polarization parameter can be easily achieved via the least squares method. The proposed method is suitable for EVS array with arbitrary geometry, and it is insensitive to the spatially colored noise. Therefore, it is more flexible than the state-of-the-art algorithms. Finally, numerical simulations are carried out to verify the effectiveness of the proposed estimator.
Highlights
Direction-of-arrival (DOA) estimation using sensor array is one of the most popular methods for source localization, and it has struck a series of technical elevations in wireless communications [1,2,3], radars, sonars, etc
Most of the current works are concentrating on one-dimensional (1D) DOA estimation
A signal electromagnetic vector sensor (EVS) consists of six collocated components, i.e., three orthogonally oriented dipoles and three orthogonally oriented loops, which measure the electric field and magnetic field, respectively
Summary
Direction-of-arrival (DOA) estimation using sensor array is one of the most popular methods for source localization, and it has struck a series of technical elevations in wireless communications [1,2,3], radars, sonars, etc. EVS array can provide 2D-DOA estimation and the polarization status of the incoming sources, which maybe important in detecting weak signal [17]. Wireless Communications and Mobile Computing nique is suitable for a single EVS Another important branch is combining the traditional subspace method with vector cross-product. The ESPRIT-like approach was investigated in [20] It firstly obtains the signal subspace via eigendecomposition, and a normalized Poynting vector is constructed; the vector cross product is performed to achieve 2D-DOA estimation. ESPRIT-like approach is suitable for arbitrary array geometry, and it offers closed-form solution for parameter estimation. Throughout the paper, lowercase letters represent vectors and uppercase letters denote matrices, respectively; IN is a N × N identity matrix; ⊗ , ⊙ , and ∗ denote the Kronecker product, the Khatri-Rao product, and the vector cross product, respectively; the superscript ðXÞT , ðXÞH, ðXÞ−1, and ðXÞ† indicate the operations of transpose, Hermitian transpose, inverse, and pseudoinverse, respectively; Dn fAg denotes a diagonal matrix with the diagonal entities which are the nth row of A; aðmÞ accounts for the mth entity of the vector a; k⋅kF accounts for the Frobenius norm; phaseðÞ is to get the phase; and Ef⋅g returns the mathematical expectation
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