Blind Joint DOA and Polarization Estimation for Polarization-Sensitive Coprime Arrays Via Reduced-Dimensional Root Finding Approach

Abstract

In this paper, we investigate the problem of blind joint multi-parameter estimation for polarization-sensitive coprime linear arrays (PS-CLAs). We propose a reduced-dimensional polynomial root finding approach, which first utilizes the relation between the two subarrays to reconstruct the spectrum function and then converts three-dimensional (3D) total spectral search (TSS) to one-dimensional (1D) TSS. Furthermore, 1D polynomial root finding technique is employed to obtain the ambiguous direction of arrival (DOA) estimates, for further saving the computational cost. Finally, the true DOA estimates can be obtained based on the arrangements with coprime property, and subsequently the polarization parameters can be estimated through pairing. In addition, the matching error of false targets can be avoided due to the relation between the two subarrays. The proposed approach only requires about 0.01% computational complexity of the 1D TSS method to achieve the same estimation performance and behaves better in resolution. Simulations are provided to validate the superiority of the proposed approach.

L-Shaped Sparse Array Structure for 2-D DOA Estimation

“…where R S can be estimated by considering the eigendecomposition of the covariance matrix R X in (14) as…”

“…where U Xs and U Xn denote the signal and noise subspace eigenvector matrices, respectively, Λ s ∈ C K×K and Λ n ∈ C (2W −1−K)×(2W −1−K) denote the diagonal matrices composed of the eigenvalues of R X with respect to U Xs and U Xn respectively. Using (14) and 29, R S can be estimated by [36]R…”

Sparse linear array with low mutual coupling ratio for DOA estimation

“…However, ULAs also have distinct disadvantages, such as smaller array aperture, less degrees-of-freedom (DOFs) and higher mutual coupling ratio compared with sparse linear arrays. As we know, more DOFs indicates that more source signals can be recognized, larger array aperture means higher resolution, lower mutual coupling ratio demonstrates less adverse effect on DOA estimation performance [15][16][17][18]. A large number of array structures have been proposed to address these shortcomings of ULAs in recent decades, such as minimum redundancy array (MRA) [19], original coprime array (OCA) [20], augmented coprime array (ACA) [21] and nested array (NA) [22].…”

“…However, ULAs also have distinct disadvantages, such as smaller array aperture, less degrees‐of‐freedom (DOFs) and higher mutual coupling ratio compared with sparse linear arrays. As we know, more DOFs indicates that more source signals can be recognized, larger array aperture means higher resolution, lower mutual coupling ratio demonstrates less adverse effect on DOA estimation performance [15–18].…”

DOA estimation of the quasi‐stationary signal using sparse reconstruction

“…Direction of arrival (DOA) estimation, also known as angle estimation, is a significant research subject in array signal processing [1][2][3][4][5][6][7][8] and has been successfully utilised in the fields such as speech, radar, astronomy, sonar, wireless communication etc. [9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Traditionally, because of the limitation of the Nyquist sampling theorem, theuniform linear array (ULA) is widely applied to DOA estimation, and a lot of DOA estimation algorithms have been proposed for ULA, such as multiple signal classification (MUSIC) [23] and estimation of the signal parameter via rotational invariance techniques (ESPRIT) [24] etc.…”