Tri-polarized Sparse Array Design for Mutual Coupling Reduction in Direction Finding and Polarization Estimation

Multi-polarized antenna arrays have the ability to provide both the direction and polarization information of the incident signals, which is important in radar, sonar, wireless communication, remote sensing, and so on. In this paper, a diversely polarized linear array of sparsely located but identically oriented tri-polarized vector antennas (VAs) is designed for estimating the direction-of-arrival (DOA) and polarization parameters of the incident signals in the presence of antenna mutual coupling (MC). In order to reduce the inter-VA MC, a new type of sparse array geometry is proposed, wherein the minimum inter-VA spacing is constrained to be no less than one signal wavelength. Considering the intra-VA MC effect, a full-wave electromagnetic simulation is introduced to fit the manifold vector of an isolated VA. Based on the sparse VA array, a polarimetric subspace scheme is proposed for DOA and polarization estimation. When the knowledge about the intra-VA MC is a priori unavailable, an algebraic polarimetric blind scheme is also provided for DOA estimation. Computer simulations and real-world experiments (using an S-band 24-channel tri-polarized array system) validate the efficacy of the designed array geometry along with the parameter estimation methods.

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Blind Direction-of-Arrival Estimation with Uniform Circular Array in Presence of Mutual Coupling

International Journal of Antennas and Propagation ◽

10.1155/2016/8109013 ◽

2016 ◽

Vol 2016 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

Song Liu ◽

Lisheng Yang ◽

Shizhong Yang ◽

Qingping Jiang ◽

Haowei Wu

Keyword(s):

Mutual Coupling ◽

Coupling Parameter ◽

Direction Of Arrival ◽

Estimation Algorithm ◽

Doa Estimation ◽

Estimation Methods ◽

Circular Array ◽

Signal Parameters ◽

Simulation Results ◽

Parameter Estimation Methods

A blind direction-of-arrival (DOA) estimation algorithm based on the estimation of signal parameters via rotational invariance techniques (ESPRIT) is proposed for a uniform circular array (UCA) when strong electromagnetic mutual coupling is present. First, an updated UCA model with mutual coupling in a discrete Fourier transform (DFT) beam space is deduced, and the new manifold matrix is equal to the product of a centrosymmetric diagonal matrix and a Vandermonde-structure matrix. Then we carry out blind DOA estimation through a modified ESPRIT method, thus avoiding the need for spatial angular searching. In addition, two mutual coupling parameter estimation methods are presented after the DOAs have been estimated. Simulation results show that the new algorithm is reliable and effective especially for closely spaced signals.

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Effect of Sparse Array Geometry on Estimation of Co-array Signal Subspace

Applied Computational Electromagnetics Society ◽

10.47037/2020.aces.j.351186 ◽

2021 ◽

Vol 35 (11) ◽

pp. 1435-1436

Author(s):

Mehmet Hucumenoglu ◽

Piya Pal

Keyword(s):

Covariance Matrix ◽

Estimation Error ◽

Doa Estimation ◽

Singular Value ◽

Signal Subspace ◽

Sparse Array ◽

Subspace Estimation ◽

Signal Covariance Matrix ◽

Special Case ◽

Array Geometry

This paper considers the effect of sparse array geometry on the co-array signal subspace estimation error for Direction-of-Arrival (DOA) estimation. The second largest singular value of the signal covariance matrix plays an important role in controlling the distance between the true subspace and its estimate. For a special case of two closely-spaced sources impinging on the array, we explicitly compute the second largest singular value of the signal covariance matrix and show that it can be significantly larger for a nested array when compared against a uniform linear array with same number of sensors.

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How Can Spatially Distributed Uncertainties Be Included in FEA and in Parameter Estimation for Model Updating?

Shock and Vibration ◽

10.1155/2003/594714 ◽

2003 ◽

Vol 10 (1) ◽

pp. 15-25 ◽

Cited By ~ 4

Author(s):

M.W. Zehn ◽

A. Saitov

Keyword(s):

Parameter Estimation ◽

Model Updating ◽

A Priori ◽

Estimation Methods ◽

Parameter Distribution ◽

Fe Model ◽

Weighting Matrix ◽

Spatially Distributed ◽

Updating Procedure ◽

Parameter Estimation Methods

Owing to manufacturing composite materials and others show considerable uncertainties in wall-thickness, fluctuations in material properties and other parameter, which are spatially distributed over the structure. These uncertainties have a random character and can therefore not being reduced by some kind of mesh refinement within the FE model. What we need is a suitable statistical approach to describe the parameter changing that holds for the statistics of the process and the correlation between the parameter spatially distributed over the structure. The paper presents a solution for a spatial correlated simulation of parameter distribution owing to the manufacturing process or other causes that is suitable to be included in the FEA. The parameter estimation methods used in updating algorithms for FE-models, depend on the choice of a priori to be determined weighting matrices. The weighting matrices are in most cases assumed by engineering judgement of the analyst carrying out the updating procedure and his assessment of uncertainty of parameters chosen and measured and calculated results. With the statistical description of the spatial distribution at hand, we can calculate a parameter weighting matrix for a Baysian estimator. Furthermore, it can be shown in principle that with model updating it is possible to improve the probabilistic parameter distribution itself.

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A Novel Data Transformation Approach for DOA Estimation With 3-D Antenna Arrays in the Presence of Mutual Coupling

IEEE Antennas and Wireless Propagation Letters ◽

10.1109/lawp.2017.2699292 ◽

2017 ◽

Vol 16 ◽

pp. 2118-2121 ◽

Cited By ~ 8

Author(s):

Ahmet M. Elbir

Keyword(s):

Antenna Arrays ◽

Mutual Coupling ◽

Doa Estimation ◽

Data Transformation ◽

Transformation Approach

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COPRIME ARRAY PARAMETERS OPTIMIZATION FOR DOA ESTIMATION

Iraqi Journal of Information & Communications Technology ◽

10.31987/ijict.4.2.131 ◽

2021 ◽

Vol 4 (2) ◽

pp. 23-32

Author(s):

Fatimah A. Salman ◽

Bayan M. Sabbar

Keyword(s):

Mutual Coupling ◽

Direction Of Arrival ◽

Doa Estimation ◽

Parameters Optimization ◽

Degree Of Freedom ◽

Direction Of Arrival Estimation ◽

Sparse Arrays ◽

Sparse Array ◽

Simulation Results ◽

Coprime Array

Sparse array such as the coprime array is one of the most preferable sparse arrays for direction of arrival estimation due to its properties, like simplicity, capability of resolving more sources than the number of elements and resistance to mutual coupling issue. In this paper, a new coprime array model is proposed to increase the number of degree of freedom (DOF) and improve the performance of coprime array. The new designed array can avoid the mutual coupling by minimizing the lag redundancy and expand the central lags in the virtual difference co-array. Thus, the propose structure can resolve more sources than the prototype coprime array using the same number of elements with improved direction of arrival estimation. Simulation results demonstrate that the proposed array model is more efficient than the others coprime array model.

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Source Localization of EM Waves in the Near-Field of Spherical Antenna Array in the Presence of Unknown Mutual Coupling

Wireless Communications and Mobile Computing ◽

10.1155/2021/3237219 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Oluwole John Famoriji ◽

Thokozani Shongwe

Keyword(s):

Antenna Array ◽

Source Localization ◽

Antenna Arrays ◽

Mutual Coupling ◽

Near Field ◽

Ground Truth ◽

Doa Estimation ◽

Measured Data ◽

Practical Applications ◽

Spherical Antenna

To obtain an antenna array with isotropic radiation, spherical antenna array (SAA) is the right array configuration. The challenges of locating signals transmitted within the proximity of antenna array have been investigated considerably in the literature. However, near-field (NF) source localization of signals has hitherto not been investigated effectively using SAA in the presence of mutual coupling (MC). MC is another critical problem in antenna arrays. This paper presents an NF range and direction-of-arrival (DoA) estimation technique via the direction-independent and signal invariant spherical harmonics (SH) characteristics in the presence of mutual coupling. The energy of electromagnetic (EM) signal on the surface of SAA is captured successfully using a proposed pressure interpolation approach. The DoA estimation within the NF region is then calculated via the distribution of pressure. The direction-independent and signal invariant characteristics, which are SH features, are obtained using the DoA estimates in the NF region. We equally proposed a learning scheme that uses the source activity detection and convolutional neural network (CNN) to estimate the range of the NF source via the direction-independent and signal invariant features. Considering the MC problem and using the DoA estimates, an accurate spectrum peak in the multipath situation in conjunction with MC and a sharper spectrum peak from a unique MC structure and smoothing algorithms are obtained. For ground truth performance evaluation of the SH features within the context of NF localization, a numerical experiment is conducted and measured data were used for analysis to incorporate the MC and consequently computed the root mean square error (RMSE) of the source range and NF DoA estimate. The results obtained from numerical experiments and measured data indicate the validity and effectiveness of the proposed approach. In addition, these results are motivating enough for the deployment of the proposed method in practical applications.

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Highly Accurate Multi-Invariance ESPRIT for DOA Estimation with a Sparse Array

Mathematical Problems in Engineering ◽

10.1155/2019/5325817 ◽

2019 ◽

Vol 2019 ◽

pp. 1-7

Author(s):

Chen Gu ◽

Hong Hong ◽

Yusheng Li ◽

Xiaohua Zhu ◽

Jin He

Keyword(s):

Doa Estimation ◽

Invariance Property ◽

Two Dimensional ◽

Sparse Array ◽

Half Wavelength ◽

Vector Sensors ◽

Matrix Blocks ◽

Simulation Results ◽

Non Gaussian ◽

Array Geometry

This paper proposes a multi-invariance ESPRIT-based method for estimation of 2D direction (MIMED) of multiple non-Gaussian monochromatic signals using cumulants. In the MIMED, we consider an array geometry containing sparse L-shaped diversely polarized vector sensors plus an arbitrarily-placed single polarized scalar sensor. Firstly, we define a set of cumulant matrices to construct two matrix blocks with multi-invariance property. Then, we develop a multi-invariance ESPRIT-based algorithm with aperture extension using the defined matrix blocks to estimate two-dimensional directions of the signals. The MIMED can provide highly accurate and unambiguous direction estimates by extending the array element spacing beyond a half-wavelength. Finally, we present several simulation results to demonstrate the superiority of the MIMED.

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