A DOA Estimation Method in the presence of unknown mutual coupling based on 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.

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Compressed Sensing-Based DOA Estimation with Unknown Mutual Coupling Effect

Electronics ◽

10.3390/electronics7120424 ◽

2018 ◽

Vol 7 (12) ◽

pp. 424 ◽

Cited By ~ 11

Author(s):

Peng Chen ◽

Zhenxin Cao ◽

Zhimin Chen ◽

Linxi Liu ◽

Man Feng

Keyword(s):

Compressed Sensing ◽

Mutual Coupling ◽

Linear Array ◽

State Of The Art ◽

Coupling Effect ◽

Estimation Method ◽

Doa Estimation ◽

System Model ◽

Reconstruction Problem ◽

Uniform Linear Array

The performance of a direction-finding system is significantly degraded by the imperfection of an array. In this paper, the direction-of-arrival (DOA) estimation problem is investigated in the uniform linear array (ULA) system with the unknown mutual coupling (MC) effect. The system model with MC effect is formulated. Then, by exploiting the signal sparsity in the spatial domain, a compressed-sensing (CS)-based system model is proposed with the MC coefficients, and the problem of DOA estimation is converted into that of a sparse reconstruction. To solve the reconstruction problem efficiently, a novel DOA estimation method, named sparse-based DOA estimation with unknown MC effect (SDMC), is proposed, where both the sparse signal and the MC coefficients are estimated iteratively. Simulation results show that the proposed method can achieve better performance of DOA estimation in the scenario with MC effect than the state-of-the-art methods, and improve the DOA estimation performance about 31.64 % by reducing the MC effect by about 4 dB.

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Tensor-Based Angle Estimation Approach for Strictly Noncircular Sources with Unknown Mutual Coupling in Bistatic MIMO Radar

Sensors ◽

10.3390/s18092788 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2788 ◽

Cited By ~ 3

Author(s):

Yuehao Guo ◽

Xianpeng Wang ◽

Wensi Wang ◽

Mengxing Huang ◽

Chong Shen ◽

...

Keyword(s):

Mutual Coupling ◽

Multiple Input Multiple Output ◽

Estimation Method ◽

Direction Of Arrival ◽

Doa Estimation ◽

Mimo Radar ◽

Angle Estimation ◽

Estimation Performance ◽

Direction Of Departure ◽

Value Decomposition

In the paper, the estimation of joint direction-of-departure (DOD) and direction-of-arrival (DOA) for strictly noncircular targets in multiple-input multiple-output (MIMO) radar with unknown mutual coupling is considered, and a tensor-based angle estimation method is proposed. In the proposed method, making use of the banded symmetric Toeplitz structure of the mutual coupling matrix, the influence of the unknown mutual coupling is removed in the tensor domain. Then, a special enhancement tensor is formulated to capture both the noncircularity and inherent multidimensional structure of strictly noncircular signals. After that, the higher-order singular value decomposition (HOSVD) technology is applied for estimating the tensor-based signal subspace. Finally, the direction-of-departure (DOD) and direction-of-arrival (DOA) estimation is obtained by utilizing the rotational invariance technique. Due to the use of both noncircularity and multidimensional structure of the detected signal, the algorithm in this paper has better angle estimation performance than other subspace-based algorithms. The experiment results verify that the method proposed has better angle estimation performance.

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Computationally Efficient 2D DOA Estimation for L-Shaped Array with Unknown Mutual Coupling

Mathematical Problems in Engineering ◽

10.1155/2018/5454719 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Yang-Yang Dong ◽

Xin Chang

Keyword(s):

Mutual Coupling ◽

Coupling Effect ◽

Estimation Method ◽

Doa Estimation ◽

Correlation Effect ◽

Complex Eigenvalue ◽

Computationally Efficient ◽

Angle Estimation ◽

Estimation Performance ◽

2D Doa Estimation

Although L-shaped array can provide good angle estimation performance and is easy to implement, its two-dimensional (2D) direction-of-arrival (DOA) performance degrades greatly in the presence of mutual coupling. To deal with the mutual coupling effect, a novel 2D DOA estimation method for L-shaped array with low computational complexity is developed in this paper. First, we generalize the conventional mutual coupling model for L-shaped array and compensate the mutual coupling blindly via sacrificing a few sensors as auxiliary elements. Then we apply the propagator method twice to mitigate the effect of strong source signal correlation effect. Finally, the estimations of azimuth and elevation angles are achieved simultaneously without pair matching via the complex eigenvalue technique. Compared with the existing methods, the proposed method is computationally efficient without spectrum search or polynomial rooting and also has fine angle estimation performance for highly correlated source signals. Theoretical analysis and simulation results have demonstrated the effectiveness of the proposed method.

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Broadband DOA Estimation Based on Nested Arrays

International Journal of Antennas and Propagation ◽

10.1155/2015/974634 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7 ◽

Cited By ~ 4

Author(s):

Zhi-bo Shen ◽

Chun-xi Dong ◽

Yang-yang Dong ◽

Guo-qing Zhao ◽

Long Huang

Keyword(s):

Degrees Of Freedom ◽

Sensor Array ◽

Frequency Estimation ◽

Estimation Method ◽

Doa Estimation ◽

Search Process ◽

Theoretic Analysis ◽

Crucial Problem ◽

Estimation Precision ◽

Nested Arrays

Direction of arrival (DOA) estimation is a crucial problem in electronic reconnaissance. A novel broadband DOA estimation method utilizing nested arrays is devised in this paper, which is capable of estimating the frequencies and DOAs of multiple narrowband signals in broadbands, even though they may have different carrier frequencies. The proposed method converts the DOA estimation of multiple signals with different frequencies into the spatial frequency estimation. Then, the DOAs and frequencies are pair matched by sparse recovery. It is possible to significantly increase the degrees of freedom (DOF) with the nested arrays and the number of sources can be more than that of sensor array. In addition, the method can achieve high estimation precision without the two-dimensional search process in frequency and angle domain. The validity of the proposed method is verified by theoretic analysis and simulation results.

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

International Journal of Antennas and Propagation ◽

10.1155/2021/1563164 ◽

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.

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A 2D Nested Array Based DOA Estimator for Incoherently Distributed Sources via Sparse Representation Utilizing L1-Norm

International Journal of Antennas and Propagation ◽

10.1155/2019/6941963 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11

Author(s):

Tao Wu ◽

Yiwen Li ◽

Zhengxin Li ◽

Yijie Huang ◽

Jiwei Xu

Keyword(s):

Sparse Representation ◽

Estimation Method ◽

Doa Estimation ◽

Rotational Invariance ◽

L1 Norm ◽

Second Order Cone ◽

Incoherently Distributed Sources ◽

Virtual Array ◽

Norm Constraint ◽

Nested Arrays

Nested arrays are sparse arrays composed of subarrays with nonuniform sensor spacing. Compared with traditional uniform arrays, nested arrays have more degree of freedoms (DOFs) and larger apertures. In this paper, a nested array has been proposed as well as a direction-of-arrival (DOA) estimation method for two-dimensional (2D) incoherently distributed (ID) sources. A virtual array is firstly obtained through vectorization of the cross-correlation matrix of subarrays. Sensor positions of the virtual array and the optimal configuration of the nested array are derived next. Then rotational invariance relationship for generalized steering matrix of the virtual array with respect to nominal azimuth is deduced. According to the rotational invariance relationship, sparse representation model under l1-norm constraint is established, which is resolved by transferring the objective function to second-order cone constraints and combining a estimation residual error constraint for receive vector of the virtual array. Simulations are conducted to investigate the effectiveness of the proposed method in underdetermined situation and examine different experiment factors including SNR, snapshots, and angular spreads as well as sensor number of subarrays. Results show that the proposed method has better performance than uniform parallel arrays with the same number of sensors.

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