Sparse DOD/DOA Estimation in a Bistatic MIMO Radar With Mutual Coupling Effect

The unknown mutual coupling effect between antennas significantly degrades the target localization performance in the bistatic multiple-input multiple-output (MIMO) radar. In this paper, the joint estimation problem for the direction of departure (DOD) and direction of arrival (DOA) is addressed. By exploiting the target sparsity in the spatial domain and formulating a dictionary matrix with discretizing the DOD/DOA into grids, compressed sensing (CS)-based system model is given. However, in the practical MIMO radar systems, the target cannot be precisely on the grids, and the unknown mutual coupling effect degrades the estimation performance. Therefore, a novel CS-based DOD/DOA estimation model with both the off-grid and mutual coupling effect is proposed, and a novel sparse reconstruction method is proposed to estimate DOD/DOA with updating both the off-grid and mutual coupling parameters iteratively. Moreover, to describe the estimation performance, the corresponding Cramér–Rao lower bounds (CRLBs) with all the unknown parameters are theoretically derived. Simulation results show that the proposed method can improve the DOD/DOA estimation in the scenario with unknown mutual coupling effect, and outperform state-of-the-art methods.

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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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Effects of bandwidth on joint estimation performance of direction-of-departure and direction-of-arrival in ultra-wideband MIMO radar

2014 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM) ◽

10.1109/usnc-ursi-nrsm.2014.6928000 ◽

2014 ◽

Author(s):

Kotaro Nagano ◽

Idnin Pasya ◽

Naohiko Iwakiri ◽

Takehiko Kobayashi

Keyword(s):

Direction Of Arrival ◽

Mimo Radar ◽

Joint Estimation ◽

Ultra Wideband ◽

Estimation Performance ◽

Direction Of Departure

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Angle Estimation for Bistatic MIMO Radar with Unknown Mutual Coupling Based on Improved Propagator Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.513-517.3029 ◽

2014 ◽

Vol 513-517 ◽

pp. 3029-3033 ◽

Cited By ~ 3

Author(s):

Jian Feng Li ◽

Wei Yang Chen ◽

Xiao Fei Zhang

Keyword(s):

Mutual Coupling ◽

Signal To Noise Ratio ◽

Multiple Input Multiple Output ◽

Doa Estimation ◽

Mimo Radar ◽

Rotational Invariance ◽

Angle Estimation ◽

Direction Of Departure ◽

Input Multiple Output ◽

Joint Direction

In this paper, joint direction of departure (DOD) and direction of arrival (DOA) estimation for multiple-input multiple-output (MIMO) radar with unknown mutual coupling is studied. An improved propagator calculation method is proposed to overcome the performance degradation problem when signal to noise ratio (SNR) is low. Thereafter, according to the Toeplitz structure of the mutual coupling matrix, the rotational invariance can be extracted for the angle estimation regardless of the mutual coupling from the augmented propagator matrix. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm and conventional PM-like method, and angles are automatically paired. The simulation results verify the effectiveness of the algorithm.

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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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Sparse-Based Millimeter Wave Channel Estimation With Mutual Coupling Effect

Electronics ◽

10.3390/electronics8030358 ◽

2019 ◽

Vol 8 (3) ◽

pp. 358 ◽

Cited By ~ 3

Author(s):

Zhenxin Cao ◽

Haiyang Geng ◽

Zhimin Chen ◽

Peng Chen

Keyword(s):

Channel Estimation ◽

Millimeter Wave ◽

Communication System ◽

Mutual Coupling ◽

Coupling Effect ◽

Sparse Signals ◽

Communication Performance ◽

Estimation Performance ◽

Wave Channel ◽

Direction Of Departure

The imperfection of antenna array degrades the communication performance in the millimeter wave (mmWave) communication system. In this paper, the problem of channel estimation for the mmWave communication system is investigated, and the unknown mutual coupling (MC) effect between antennas is considered. By exploiting the channel sparsity in the spatial domain with mmWave frequency bands, the problem of channel estimation is converted into that of sparse reconstruction. The MC effect is described by a symmetric Toeplitz matrix, and the sparse-based mmWave system model with MC coefficients is formulated. Then, a two-stage method is proposed by estimating the sparse signals and MC coefficients iteratively. Simulation results show that the proposed method can significantly improve the channel estimation performance in the scenario with unknown MC effect and the estimation performance for both direction of arrival (DOA) and direction of departure (DoD) can be improved by about 8 dB by reducing the MC effect about 4 dB.

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IMPROVED SPECTRUM SEARCHING GENERALIZED–ESPRIT ALGORITHM FOR JOINT DOD AND DOA ESTIMATION IN MIMO RADAR WITH NON-UNIFORM LINEAR ARRAYS

Journal of Circuits System and Computers ◽

10.1142/s0218126614501060 ◽

2014 ◽

Vol 23 (08) ◽

pp. 1450106 ◽

Cited By ~ 1

Author(s):

WEIYANG CHEN ◽

XIAOFEI ZHANG

Keyword(s):

Multiple Input Multiple Output ◽

Estimation Algorithm ◽

Doa Estimation ◽

Mimo Radar ◽

Joint Estimation ◽

Angle Estimation ◽

Linear Arrays ◽

Half Wavelength ◽

Direction Of Departure ◽

Esprit Algorithm

This paper investigates the problem of angle estimation for bistatic multiple-input multiple-output (MIMO) radar with non-uniform linear arrays, and proposes an improved spectrum searching generalized estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm for joint direction of departure (DOD) and direction of arrival (DOA) estimation algorithm in bistatic MIMO radar. The proposed algorithm obtains initial estimation of angles obtained from the signal subspace, and uses the 1D local searchings to achieve the joint estimation of DOD and DOA. Compared to the spectrum searching generalized-ESPRIT algorithm which requires the global searchings and additional pairing, the proposed algorithm just needs the local searchings and obtains automatically paired 2D angle estimation. The angle estimation performance of the proposed algorithm is almost the same as that of the generalized-ESPRIT algorithm, and better than ESPRIT-like algorithm. Furthermore, the proposed algorithm is suitable for irregular array geometry, has much lower complexity than the spectrum searching generalized-ESPRIT algorithm, and imposes less constraint on the transmit/receive sensor spacing, which need not be limited to a half-wavelength strictly. The simulation results verify the effectiveness of the algorithm.

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Efficient Two-Dimensional Direction Finding Algorithm for Rectilinear Sources Under Unknown Mutual Coupling

Sensors ◽

10.3390/s20071914 ◽

2020 ◽

Vol 20 (7) ◽

pp. 1914

Author(s):

Jian Xie ◽

Qiuping Wang ◽

Yuexian Wang ◽

Xin Yang

Keyword(s):

Degrees Of Freedom ◽

Mutual Coupling ◽

Array Signal Processing ◽

Elevation Angle ◽

Doa Estimation ◽

Two Dimensional ◽

Rank Reduction ◽

Communication Signals ◽

Estimation Performance ◽

Direction Cosines

Digital communication signals in wireless systems may possess noncircularity, which can be used to enhance the degrees of freedom for direction-of-arrival (DOA) estimation in sensor array signal processing. On the other hand, the electromagnetic characteristics between sensors in uniform rectangular arrays (URAs), such as mutual coupling, may significantly deteriorate the estimation performance. To deal with this problem, a robust real-valued estimator for rectilinear sources was developed to alleviate unknown mutual coupling in URAs. An augmented covariance matrix was built up by extracting the real and imaginary parts of observations containing the circularity and noncircularity of signals. Then, the actual steering vector considering mutual coupling was reparameterized to make the rank reduction (RARE) property available. To reduce the computational complexity of two-dimensional (2D) spectral search, we individually estimated y-axis and x-axis direction-cosines in two stages following the principle of RARE. Finally, azimuth and elevation angle estimates were determined from the corresponding direction-cosines respectively. Compared with existing solutions, the proposed method is more computationally efficient, involving real-valued operations and decoupled 2D spectral searches into twice those of one-dimensional searches. Simulation results verified that the proposed method provides satisfactory estimation performance that is robust to unknown mutual coupling and close to the counterparts based on 2D spectral searches, but at the cost of much fewer calculations.

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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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