scholarly journals Distributed Super Nested Arrays: Reduce the Mutual Coupling between Array Antennas

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Hongyong Wang ◽  
Weibo Deng ◽  
Ying Suo ◽  
Xin Zhang ◽  
Yanmo Hu ◽  
...  
Keyword(s):  
Mutual Coupling ◽  
Doa Estimation ◽  
Baseline Length ◽  
Spatial Smoothing ◽  
Estimation Of Parameters ◽  
Signal Parameter ◽  
Array Antennas ◽  
Esprit Algorithm ◽  
Nested Arrays ◽  
Source Signals

In array, mutual coupling between the antennas is inevitable, which has an adverse effect on the estimation of parameters. To reduce the mutual coupling between the antennas of distributed nested arrays, this paper proposes a new array called the distributed super nested arrays, which have the good characteristics of the distributed nested arrays and can reduce the mutual coupling between the antennas. Then, an improved multiscale estimating signal parameter via rotational invariance techniques (ESPRIT) algorithm is presented for the distributed super nested arrays to improve the accuracy of direction-of-arrival (DOA) estimation. Next, we analyze the limitations of the spatial smoothing algorithm used by the distributed super nested arrays when there are multiple-source signals and the influence of the baseline length of distributed super nested arrays on the accuracy of DOA estimation. The simulation results show that the distributed super nested arrays can effectively reduce the mutual coupling between the array antennas, improve the DOA estimation performance, and significantly increase the number of detectable source signals.

scholarly journals Thermos Array: Two-Dimensional Sparse Array with Reduced Mutual Coupling

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Lei Sun ◽  
Minglei Yang ◽  
Baixiao Chen
Keyword(s):  
Closed Form ◽  
Degrees Of Freedom ◽  
Mutual Coupling ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Spatial Smoothing ◽  
Planar Arrays ◽  
Half Wavelength ◽  
Planar Array ◽  
Small Spacing

Sparse planar arrays, such as the billboard array, the open box array, and the two-dimensional nested array, have drawn lots of interest owing to their ability of two-dimensional angle estimation. Unfortunately, these arrays often suffer from mutual-coupling problems due to the large number of sensor pairs with small spacing d (usually equal to a half wavelength), which will degrade the performance of direction of arrival (DOA) estimation. Recently, the two-dimensional half-open box array and the hourglass array are proposed to reduce the mutual coupling. But both of them still have many sensor pairs with small spacing d, which implies that the reduction of mutual coupling is still limited. In this paper, we propose a new sparse planar array which has fewer number of sensor pairs with small spacing d. It is named as the thermos array because its shape seems like a thermos. Although the resulting difference coarray (DCA) of the thermos array is not hole-free, a large filled rectangular part in the DCA can be facilitated to perform spatial-smoothing-based DOA estimation. Moreover, it enjoys closed-form expressions for the sensor locations and the number of available degrees of freedom. Simulations show that the thermos array can achieve better DOA estimation performance than the hourglass array in the presence of mutual coupling, which indicates that our thermos array is more robust to the mutual-coupling array.

scholarly journals DOA Estimation Using Fourth-Order Cumulants in Nested Arrays with Structured Imperfections

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 994
Author(s):  
Baoping Wang ◽  
Junhao Zheng
Keyword(s):  
Fourth Order ◽  
Mutual Coupling ◽  
Estimation Method ◽  
Doa Estimation ◽  
Coupling Model ◽  
Coupling Coefficients ◽  
Rank Reduction ◽  
Phase Errors ◽  
Reduction Property ◽  
Nested Arrays

Recently developed super nested array families have drawn much attention owing to their merits on keeping the benefits of the standard nested arrays while further mitigating coupling in dense subarray portions. In this communication, a new mutual coupling model for nested arrays is constructed. Analyzing the structure of the newly formed mutual coupling matrix, a transformation of the distorted steering vector to separate angular information from the mutual coupling coefficients is revealed. By this property, direction of arrival (DOA) estimates can be determined via a grid search for the minimum of a determinant function of DOA, which is induced by the rank reduction property. We also extend the robust DOA estimation method to accommodate the unknown mutual coupling and gain-phase mismatches in the nested array. Compared with the schemes of super nested array families on reducing the mutual coupling effects, the solutions presented in this paper has two advantages: (a) It is applicable to the standard nested arrays without rearranging the configuration to increase the inter-element spacing, alleviating the cross talk in dense uniform linear arrays (ULAs) as well as gain-phase errors in sparse ULA parts; (b) Perturbations in nested arrays are estimated in colored noise, which is significant but rarely discussed before. Simulations results corroborate the superiority of the proposed methods using fourth-order cumulants.

A Developed ESPRIT Algorithm for DOA Estimation

Frequenz ◽  
2015 ◽  
Vol 69 (5-6) ◽  
Author(s):  
Youssef Fayad ◽  
Caiyun Wang ◽  
Qunsheng Cao ◽  
Alaa El-Din Sayed Hafez
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Performance Enhancement ◽  
Doa Estimation ◽  
Signal Parameter ◽  
Nonlinearity Effect ◽  
Esprit Algorithm ◽  
Cramer Rao Bound ◽  
Novel Algorithm ◽  
Better Than

AbstractA novel algorithm for estimating direction of arrival (DOAE) for target, which aspires to contribute to increase the estimation process accuracy and decrease the calculation costs, has been carried out. It has introduced time and space multiresolution in Estimation of Signal Parameter via Rotation Invariance Techniques (ESPRIT) method (TS-ESPRIT) to realize subspace approach that decreases errors caused by the model’s nonlinearity effect. The efficacy of the proposed algorithm is verified by using Monte Carlo simulation, the DOAE accuracy has evaluated by closed-form Cramér–Rao bound (CRB) which reveals that the proposed algorithm’s estimated results are better than those of the normal ESPRIT methods leading to the estimator performance enhancement.

scholarly journals Joint Estimation of DOA and Frequency of Multiple Sources with Orthogonal Coprime Arrays

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 335 ◽  
Author(s):  
Kai-Chieh Hsu ◽  
Jean-Fu Kiang
Keyword(s):  
Carrier Frequency ◽  
Joint Estimation ◽  
Orthogonal Complement ◽  
Spatial Smoothing ◽  
Multiple Sources ◽  
Music Algorithm ◽  
Two Stage ◽  
Coprime Arrays ◽  
Esprit Algorithm ◽  
Source Signals

A two-stage method is proposed to jointly estimate the direction-of-arrival (DOA) and carrier frequency (CF) of multiple sources, by using two orthogonal coprime arrays (CPAs). The DOAs of CF-known sources are estimated first by applying a spatial smoothing MUSIC algorithm. The contribution of these source signals is then removed from the originally received signal by applying an orthogonal complement projector. Next, a joint-ESPRIT algorithm is applied to estimate the DOAs and CFs of the remaining CF-unknown sources. With two orthogonal CPA(5, 6), the RMSE of DOA and CF of applying the proposed method to 30 sources, 13 of which have unknown CF, is less than 1% at SNR > 5 dB.

scholarly journals A Robust Direction of Arrival Estimation Method for Uniform Circular Array

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4427
Author(s):  
Xu ◽  
Wu ◽  
Yu ◽  
Guang
Keyword(s):  
Array Signal Processing ◽  
Linear Array ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Circular Array ◽  
Source Signal ◽  
Random Sample Consensus ◽  
Esprit Algorithm ◽  
Source Signals

Estimating the Direction of Arrival (DOA) is a basic and crucial problem in array signal processing. The existing DOA methods fail to obtain reliable and accurate results when noise and reverberation occur in real applications. In this paper, an accurate and robust estimation method for estimating the DOA of sources signal is proposed. Incorporating the Estimating Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm with the RANdom SAmple Consensus (RANSAC) algorithm gives rise to the RAN-ESPRIT method, which removes outliers automatically in noise-corrupted environments. In this work, a uniform circular array (UCA) is converted into a virtual uniform linear array (ULA) to begin with. Then, the covariance matrix of the received signals of the virtual linear array is reconstructed, and the ESPRIT algorithm is deployed to estimate initial DOA of the source signal. Finally, the modified RANSAC method with automatically selected thresholds is used to fit the source signal to obtain accurate DOA. The proposed method can remove the unreliable DOA feature data and leads to more accuracy of DOA estimation of source signals in reverberation environments. Experimental results demonstrate that the proposed method is more robust and efficient compared to the traditional methods (i.e., ESPRIT, TLS-ESPRIT).

scholarly journals Transmit/Receive Spatial Smoothing with Improved Effective Array Aperture for Angle and Mutual Coupling Estimation in Bistatic MIMO Radar

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Haomiao Liu ◽  
Xiaojun Yang ◽  
Rong Wang ◽  
Wei Jin ◽  
Weimin Jia
Keyword(s):  
Mutual Coupling ◽  
Linear Array ◽  
Mimo Radar ◽  
Coupling Coefficients ◽  
Spatial Smoothing ◽  
Bistatic Mimo Radar ◽  
Temporal Domain ◽  
Effective Aperture ◽  
Array Aperture ◽  
Esprit Algorithm

We proposed a transmit/receive spatial smoothing with improved effective aperture approach for angle and mutual coupling estimation in bistatic MIMO radar. Firstly, the noise in each channel is restrained, by exploiting its independency, in both the spatial domain and temporal domain. Then the augmented transmit and receive spatial smoothing matrices with improved effective aperture are obtained, by exploiting the Vandermonde structure of steering vector with uniform linear array. The DOD and DOA can be estimated by utilizing the unitary ESPRIT algorithm. Finally, the mutual coupling coefficients of both the transmitter and the receiver can be figured out with the estimated angles of DOD and DOA. Numerical examples are presented to verify the effectiveness of the proposed method.

scholarly journals Direction of Arrival Estimation Using Augmentation of Coprime Arrays

Information ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 277 ◽  
Author(s):  
Tehseen Hassan ◽  
Fei Gao ◽  
Babur Jalal ◽  
Sheeraz Arif
Keyword(s):  
Covariance Matrix ◽  
Degrees Of Freedom ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Spatial Smoothing ◽  
Multiple Signal Classification ◽  
Coprime Arrays ◽  
The Difference ◽  
Source Signals

Recently, direction of arrival (DOA) estimation premised on the sparse arrays interpolation approaches, such as co-prime arrays (CPA) and nested array, have attained extensive attention because of the effectiveness and capability of providing higher degrees of freedom (DOFs). The co-prime array interpolation approach can detect O(MN) paths with O(M + N) sensors in the array. However, the presence of missing elements (holes) in the difference coarray has limited the number of DOFs. To implement co-prime coarray on subspace based DOA estimation algorithm namely multiple signal classification (MUSIC), a reshaping operation followed by the spatial smoothing technique have been presented in the literature. In this paper, an active coarray interpolation (ACI) is proposed to efficiently recovering the covariance matrix of the augmented coarray from the original covariance matrix of source signals with no vectorizing and spatial smoothing operation; thus, the computational complexity reduces significantly. Moreover, the numerical simulations of the proposed ACI approach offers better performance compared to its counterparts.

Single-Snapshot DoA Estimation in Array Antennas With Mutual Coupling Through a Multiscaling BCS Strategy

2017 ◽  
Vol 65 (6) ◽  
pp. 3203-3213 ◽  
Author(s):  
Paolo Rocca ◽  
Mohammad Abdul Hannan ◽  
Marco Salucci ◽  
Andrea Massa
Keyword(s):  
Mutual Coupling ◽  
Doa Estimation ◽  
Array Antennas ◽  
Single Snapshot
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