scholarly journals Root-MUSIC Based Angle Estimation for MIMO Radar with Unknown Mutual Coupling

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jianfeng Li ◽  
Xiaofei Zhang ◽  
Weiyang Chen
Keyword(s):  
Mutual Coupling ◽  
Estimation Error ◽  
Multiple Input Multiple Output ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Angle Estimation ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Lower Complexity ◽  
Toeplitz Structure

Direction of arrival (DOA) estimation problem for multiple-input multiple-output (MIMO) radar with unknown mutual coupling is studied, and an algorithm for the DOA estimation based on root multiple signal classification (MUSIC) is proposed. Firstly, according to the Toeplitz structure of the mutual coupling matrix, output data of some specified sensors are selected to eliminate the influence of the mutual coupling. Then the reduced-dimension transformation is applied to make the computation burden lower as well as obtain a Vandermonde structure of the direction matrix. Finally, Root-MUSIC can be adopted for the angle estimation. 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 MUSIC-like algorithm. Furthermore, the proposed algorithm has lower complexity than them. The simulation results verify the effectiveness of the algorithm, and the theoretical estimation error of the algorithm is also derived.

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

2014 ◽  
Vol 513-517 ◽  
pp. 3029-3033 ◽  
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.

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

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2788 ◽  
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.

scholarly journals Joint Angles and Mutual Coupling Estimation Algorithm for Bistatic MIMO Radar

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Jianfeng Li ◽  
Xiaofei Zhang
Keyword(s):  
Mutual Coupling ◽  
Multiple Input Multiple Output ◽  
Computational Cost ◽  
Estimation Algorithm ◽  
Mimo Radar ◽  
Coupling Coefficients ◽  
Angle Estimation ◽  
One Dimensional ◽  
Input Multiple Output ◽  
The One

We study the problem of angle estimation for a bistatic multiple-input multiple-output (MIMO) radar with unknown mutual coupling and proposed a joint algorithm for angles and mutual coupling estimation with the characteristics of uniform linear arrays and subspaces exploitation. We primarily obtain an initial estimate of DOA and DOD, then employ the local one-dimensional searching to estimate exactly DOA and DOD, and finally evaluate the parameters of mutual coupling coefficients via the estimated angles. Exploiting twice of the one-dimensional local searching, our method has much lower computational cost than the algorithm in (Liu and Liao (2012)), and automatically obtains the paired two-dimensional angle estimation. Slightly better performance for angle estimation has been achieved via our scheme in contrast to (Liu and Liao (2012)), while the two methods indicate very close performance of mutual coupling estimation. The simulation results verify the algorithmic effectiveness of our scheme.

scholarly journals DOA Estimation for Multiple Targets in MIMO Radar with Nonorthogonal Signals

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Zhenxin Cao ◽  
Peng Chen ◽  
Zhimin Chen ◽  
Yi Jin
Keyword(s):  
Correlation Matrix ◽  
Cross Correlation ◽  
Multiple Input Multiple Output ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Signal Classification ◽  
Multiple Signal Classification ◽  
Estimation Performance ◽  
Multiple Input ◽  
Input Multiple Output

This paper addresses the direction of arrival (DOA) estimation problem in the colocated multiple-input multiple-output (MIMO) radar with nonorthogonal signals. The maximum number of targets that can be estimated is theoretically derived as rankRsN, where N denotes the number of receiving antennas and Rs is the cross-correlation matrix of the transmitted signals. Therefore, with the rank-deficient cross-correlation matrix, the maximum number that can be estimated is less than the radar with orthogonal signals. Then, a multiple signal classification- (MUSIC-) based algorithm is given for the nonorthogonal signals. Furthermore, the DOA estimation performance is also theoretically analyzed by the Carmér-Rao lower bound. Simulation results show that the nonorthogonality degrades the DOA estimation performance only in the scenario with the rank-deficient cross-correlation matrix.

scholarly journals Computationally Efficient Unitary ESPRIT Algorithm in Bistatic MIMO Radar

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Baobao Liu ◽  
Tao Xue ◽  
Cong Xu ◽  
Yongjun Liu
Keyword(s):  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Mimo Radar ◽  
Computational Effort ◽  
Estimation Accuracy ◽  
Computationally Efficient ◽  
Angle Estimation ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Esprit Algorithm

A low complexity unitary estimating signal parameter via rotational invariance techniques (ESPRIT) algorithm is presented for angle estimation in bistatic multiple-input-multiple-output (MIMO) radar. The devised algorithm only requires calculating two submatrices covariance matrix, which reduces the computation cost in comparison with subspace methods. Moreover, the signal subspace can be efficiently acquired by exploiting the NystrÖm method, which only needs O M N K 2 flops. Thus, the presented algorithm has an essentially diminished computational effort, especially useful when K ≪ M N , while it can achieve efficient angle estimation accuracy as well as the existing algorithms. Several theoretical analysis and simulation results are provided to demonstrate the usefulness of the proposed scheme.

An Expanded Trilinear Model-Based Angle Estimation Algorithm for MIMO Radar with Small Number of Transmit/Receive Antennas

2016 ◽  
Vol 25 (05) ◽  
pp. 1650043 ◽  
Author(s):  
Shu Li ◽  
Weihua Lv ◽  
Xiaofei Zhang ◽  
Dazhuan Xu
Keyword(s):  
Multiple Input Multiple Output ◽  
Estimation Algorithm ◽  
Mimo Radar ◽  
Angle Estimation ◽  
Trilinear Decomposition ◽  
Trilinear Model ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Pair Matching ◽  
Esprit Algorithm

In this paper, we address the problem of angle estimation in a bistatic multiple-input multiple-output (MIMO) radar which exploits nonuniform linear array at both the transmitter and the receiver with small number of antennas. It is demonstrated that the conventional trilinear decomposition-based angle estimation algorithm can identify only a comparatively small number of targets under this condition. In order to increase the number of identifiable targets, we derive an expanded trilinear decomposition-based angle estimation algorithm for MIMO radar, which can expand the size of the trilinear model. The proposed algorithm not only has the advantages of not requiring spectral peak searching, nor additional pair matching and being suitable for nonuniform arrays, but also identifies more targets than the conventional trilinear decomposition-based angle estimation algorithm under the same conditions. Moreover, the angle estimation performance of the proposed algorithm is better than that of the conventional trilinear decomposition-based angle estimation algorithm and the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Simulation results illustrate the effectiveness and improvement of the proposed algorithm.

scholarly journals Direction Finding for Bistatic MIMO Radar with Uniform Circular Array

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Cao Yunhe ◽  
Zhang Zijing ◽  
Wang Shenghua ◽  
Dai Fengzhou
Keyword(s):  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Search Method ◽  
Circular Array ◽  
Angle Estimation ◽  
Steering Vector ◽  
Direction Of Departure ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Polynomial Rooting

A method of direction of arrival (DOA) and direction of departure (DOD) angle estimation based on polynomial rooting for bistatic multiple-input multiple-output (MIMO) radar with uniform circular array (UCA) configuration is proposed in this paper. The steering vector of the UCA is firstly transformed into a steering vector with a Vandermonde structure by using the Jacobi-Anger expansion. Then the null-spectrum function of the MIMO radar can be written as an expression in which the transmit and receive steering vectors are decoupled. Finally, a two-step polynomial rooting is used to estimate DOA and DOD of targets instead of two-dimensional multiple signal classification (MUSIC) search method for bistatic UCA MIMO radar. The angle estimation performance of the proposed method is similar to that of the MUSIC spectral search method, but the computation burden of the proposed polynomial rooting algorithm is much lower than that of the conventional MUSIC method. The simulation results of the proposed algorithm are presented and the performances are investigated and analyzed.

scholarly journals Spatial Differencing Reconstruction Based Low-Angle DOA Estimation with MIMO Radar

2018 ◽  
Vol 36 (1) ◽  
pp. 182-188
Author(s):  
Qin Zhang ◽  
Linrang Zhang ◽  
Junpeng Shi ◽  
Yannian Zhou ◽  
Yaning Liu
Keyword(s):  
Multiple Input Multiple Output ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Spatial Diversity ◽  
Mimo Radar ◽  
Estimation Accuracy ◽  
Signal Model ◽  
Estimation Performance ◽  
Multiple Input ◽  
Input Multiple Output

Due to the multipath effect, the direction of arrival (DOA) estimation performance for low-angle targets decreases greatly. To overcome this problem, this paper proposes a spatial differencing reconstruction based DOA estimation algorithm by using the received signal model of multiple input multiple output (MIMO) radar. Combining with the spatial diversity of MIMO radar, the proposed method can first use the multipath echo power to select the best signal. Then, a spatial differencing based iterative scheme is developed to reduce the effect of additive noise, resulting in a better estimation performance for low-angle targets. Simulation results show that the proposed method has better advantages in suppressing noise and improving estimation accuracy.

Compressive Sensing-Based Angle Estimation for MIMO Radar with Multiple Snapshots

2013 ◽  
Vol 347-350 ◽  
pp. 1028-1032
Author(s):  
Jian Feng Li ◽  
Xiao Fei Zhang ◽  
Tong Hu
Keyword(s):  
Compressive Sensing ◽  
Complexity Analysis ◽  
Matching Pursuit ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Eigenvalue Decomposition ◽  
Angle Estimation ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Esprit Algorithm

The issue of angle estimation for multiple-input multiple-output (MIMO) radar is studied and an algorithm for the estimation based on compressive sensing with multiple snapshots is proposed. The dimension of received signal is reduced to make the computation burden lower, and then the noise sensitivity is reduced by the eigenvalue decomposition (EVD) of the covariance matrix of the reduced-dimensional signal. Finally the signal subspace obtained from the eigenvectors is realigned to apply the orthogonal matching pursuit (OMP) for angle estimation. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, and reduced-dimension Capon. Furthermore, the proposed algorithm works well for coherent targets, and requires no knowledge of the noise. The complexity analysis and simulation results verify the effectiveness of the algorithm.

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