Virtual Array Interpolation for 2-D DOA and Polarization Estimation using Coprime EMVS Array via Tensor Nuclear Norm Minimization

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Detail(s)

Original languageEnglish
Pages (from-to)3637-3650
Journal / PublicationIEEE Transactions on Signal Processing
Volume71
Online published29 Sept 2023
Publication statusPublished - 2023

Abstract

In this paper, we develop an interpolation-based algorithm for two-dimensional (2-D) direction-of-arrival (DOA) and polarization estimation with coprime electromagnetic vectorsensor (EMVS) array. First of all, we derive the tensor form coarray output of coprime EMVS array, and perform virtual array interpolation on the output components of the difference coarray. Subsequently, we construct a low-rank third-order augmented tensor using the interpolated uniform linear array output, and derive two important properties for this low-rank tensor in the Fourier domain. Based on these properties, we reconstruct a noise-free third-order augmented tensor by formulating a tensor nuclear norm (TNN) minimization problem. Finally, we derive the closed-form expressions of 2-D DOA and polarization estimates using the reconstructed tensor. Unlike the existing techniques, our approach not only avoids losses in array aperture and degrees-of-freedom, but also exploits the multidimensional structure inherent in the coarray output. Numerical results demonstrate the superiority of the proposed algorithm over the existing approaches. © 2023 IEEE

Research Area(s)

  • 2-D DOA estimation, Coprime EMVS array, Direction-of-arrival estimation, Estimation, Interpolation, Minimization, polarization estimation, Sensor arrays, Signal processing algorithms, tensor nuclear norm (TNN), Tensors, virtual array interpolation

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