Coarray Interpolation for DOA Estimation Using Coprime EMVS Array

Fu, Mingcheng; Zheng, Zhi; Wang, Wen-Qin; So, Hing Cheung

doi:10.1109/lsp.2021.3061272

Cited by 36 publications

(14 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4: Substitute the 2D-DOA estimation result θ; φ into Q (θ, ϕ) and estimate the Jones vector ĝ utilising Equation ( 13). 5: Obtain the closed form estimation results of auxiliary angle γ and polarisation phase difference η by using Equations (14,15).…”

Section: Algorithm 1 Compressed Rd-musicmentioning

confidence: 99%

“…Although the pairing process is avoided, it still requires a lot of iterations. In addition, there are many algorithms for the non-uniform EMVSs array such as in [13][14][15]. In addition, the spatially separate EMVS is another important technology to extend the aperture of the array and reduce the mutual coupling effect compared with the spatially collocated EMVS, for instance, [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Joint 2D‐DOA and polarization estimation for Electromagnetic vector sensors array with compressive measurements

Zhu

Cheng

Chen

2022

IET Radar Sonar & Navi

View full text Add to dashboard Cite

Electromagnetic vector sensors (EMVS) arrays have been employed extensively in the field of array signal processing for their advantage in terms of polarization diversity. However, with the introduction of EMVS, the cost of the hardware equipment and the complexity of the corresponding parameter estimation algorithms increase considerably, as the number of received signal channels and the dimension of the received signal are much larger than the traditional scalar array. In order to effectively reduce hardware cost and algorithm complexity, we propose a scheme that combines an electromagnetic vector sensor array with a compression network. We construct the corresponding signal model and based on this we derive a Compressed Reduced Dimensional MUltiple SIgnal Classification (Compressed To avoid the multi-dimensional search, Reduced Dimension MUSIC) algorithm which can effectively reduce the computational complexity. While selecting the coefficient matrix of the compressed network, random selection can cause information loss, which leads to the performance degradation of the estimation algorithm. To address this problem, we propose an optimization method for coefficient matrix selection based on the maximum signal-to-noise ratio (SNR) criterion. Numerical simulations are conducted in different scenarios to verify the effectiveness of the parameter estimation algorithm and the optimization algorithm.

show abstract

Section: Algorithm 1 Compressed Rd-musicmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint 2D‐DOA and polarization estimation for Electromagnetic vector sensors array with compressive measurements

Zhu

Cheng

Chen

2022

IET Radar Sonar & Navi

View full text Add to dashboard Cite

show abstract

“…where N MC denotes the number of simulations. The RMSE is measured in degree, and has been used in many literatures [2,18,34]. The RMSE measures the average error of the DOA estimation for multiple received signals.…”

Section: Cramér-rao Bound (Crb) For the Doamentioning

confidence: 99%

“…Moreover, to achieve a better DOA estimation performance and reduce the number of receiving channels, sparse arrays have been proposed, such as the nested array [32], coprime array [33,34], etc. In [35], a dilated array is presented and applies the dilation method to other array geometries on a moving platform.…”

Section: Introductionmentioning

confidence: 99%

Efficient DOA Estimation Method for Reconfigurable Intelligent Surfaces Aided UAV Swarm

Chen,

Zheng

et al. 2022

Preprint

View full text Add to dashboard Cite

The conventional direction of arrival (DOA) estimation methods are performed with multiple receiving channels. In this paper, a changeling DOA estimation problem is addressed in a different scenario with only one full-functional receiving channel. A new unmanned aerial vehicle (UAV) swarm system using multiple lifted reconfigurable intelligent surface (RIS) is proposed for the DOA estimation. The UAV movement degrades the DOA estimation performance significantly, and the existing atomic norm minimization (ANM) methods cannot be used in the scenario with array perturbation. Specifically, considering the position perturbation of UAVs, a new atomic norm-based DOA estimation method is proposed, where an atomic norm is defined with the parameter of the position perturbation. Then, a customized semi-definite programming (SDP) method is derived to solve the atomic norm-based method, where different from the traditional SDP method, an additional transforming matrix is formulated. Moreover, a gradient descent method is applied to refine the estimated DOA and the position perturbation further. Simulation results show that the proposed method achieves much better DOA estimation performance in the RIS-aided UAV swarm system with only one receiving channel than various benchmark schemes.

show abstract

“…Given the fact that the signal sources in practical applications like radar and wireless communications are usually diversely polarized, it is expected that the parameter estimation performance can be significantly improved with the use of coprime electromagnetic vector sensor (EMVS) arrays since an EMVS can simultaneously measure all six electromagnetic field components. In fact, methods for DOA estimation using coprime EMVS arrays have been developed recently in [39] and [40]. However, these two works exploit only the increased DOFs of the spatial coarray domain.…”

Section: Introductionmentioning

confidence: 99%

Coarray Polarization Smoothing for DOA Estimation with Coprime Vector Sensor Arrays

Wang

Shu

et al. 2022

Circuits Syst Signal Process

View full text Add to dashboard Cite

In this paper, a new scheme is proposed to estimate the directions-of-arrival (DOAs) of multiple sources using a coprime electromagnetic vector sensor (EMVS) array. Each EMVS consists of three mutually orthogonal electric dipoles and three mutually orthogonal magnetic loops, all collocated at a single point in space. By exploiting the polarization diversities embedded in the vector sensors, the COarray Polarization Smoothing (COPS) is presented to increase the degrees of freedom (DOFs) from the polarization coarray domain. In contrast to the widely used spatial smoothing technique, the COPS offers the following new insights: 1) it is source polarization-dependent and is proved to offer 20 DOFs at most, 2) it does not reduce the efficient spatial coarray aperture of the coprime array so that it enables the use of all virtual coarray sensors without resorting to the nontrivial sparse recovery or array interpolation operations, and 3) it can be synthesized with the spatial smoothing to get the DOFs multiplied. Finally, the efficacy of the COPS scheme is verified by numerical examples.

show abstract

Coarray Interpolation for DOA Estimation Using Coprime EMVS Array

Cited by 36 publications

References 29 publications

Joint 2D‐DOA and polarization estimation for Electromagnetic vector sensors array with compressive measurements

Joint 2D‐DOA and polarization estimation for Electromagnetic vector sensors array with compressive measurements

Efficient DOA Estimation Method for Reconfigurable Intelligent Surfaces Aided UAV Swarm

Coarray Polarization Smoothing for DOA Estimation with Coprime Vector Sensor Arrays

Contact Info

Product

Resources

About