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.

Study on the Compressed Matrices in Compressed Sensing Trilinear Model

2014 ◽  
Vol 556-562 ◽  
pp. 3380-3383 ◽  
Author(s):  
Shu Li ◽  
Xiao Fei Zhang
Keyword(s):  
Compressed Sensing ◽  
Multiple Input Multiple Output ◽  
Estimation Algorithm ◽  
Mimo Radar ◽  
Angle Estimation ◽  
Trilinear Decomposition ◽  
Trilinear Model ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Simulation Results

In this paper, we make study on the compressed matrices in the compressed sensing trilinear model-based angle estimation algorithm, whose complexity is lower than conventional trilinear decomposition-based method, due to the use of compressed matrices. And we take the problem of angle estimation for bistatic multiple-input multiple-output (MIMO) radar as an example. Simulation results can provide reference for the choice of compressed matrices.

Compressed Sensing Trilinear Model-Based Angle Estimation for Bistatic MIMO Radar

2013 ◽  
Vol 347-350 ◽  
pp. 1033-1038 ◽  
Author(s):  
Xiao Fei Zhang ◽  
Jian Feng Li ◽  
Ming Zhou ◽  
De Ben
Keyword(s):  
Compressed Sensing ◽  
Multiple Input Multiple Output ◽  
Estimation Algorithm ◽  
Mimo Radar ◽  
Estimation Problem ◽  
Angle Estimation ◽  
Model Based ◽  
Trilinear Model ◽  
Input Multiple Output ◽  
Esprit Algorithm

In this paper, we address the transmit angle and receive angle estimation problem for a bistatic multiple-input multiple-output (MIMO) radar. This paper links MIMO radar angle estimation problem to the compressed sensing trilinear model. Exploiting this link, it derives a compressed sensing trilinear model-based angle estimation algorithm, which can obtain automatically paired two-dimensional angle estimation. The proposed algorithm requires no spectral peak searching or pair matching, and it has better angle estimation performance than conventional algorithms including estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Simulation results illustrate performance of the algorithm.

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.

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.

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.

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 Beamspace Unitary ESPRIT Algorithm for Angle Estimation in Bistatic MIMO Radar

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Dang Xiaofang ◽  
Chen Baixiao ◽  
Yang Minglei ◽  
Zheng Guimei
Keyword(s):  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Mimo Radar ◽  
Element Space ◽  
Bistatic Mimo Radar ◽  
Direction Of Departure ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Joint Direction ◽  
Esprit Algorithm

The beamspace unitary ESPRIT (B-UESPRIT) algorithm for estimating the joint direction of arrival (DOA) and the direction of departure (DOD) in bistatic multiple-input multiple-output (MIMO) radar is proposed. The conjugate centrosymmetrized DFT matrix is utilized to retain the rotational invariance structure in the beamspace transformation for both the receiving array and the transmitting array. Then the real-valued unitary ESPRIT algorithm is used to estimate DODs and DOAs which have been paired automatically. The proposed algorithm does not require peak searching, presents low complexity, and provides a significant better performance compared to some existing methods, such as the element-space ESPRIT (E-ESPRIT) algorithm and the beamspace ESPRIT (B-ESPRIT) algorithm for bistatic MIMO radar. Simulation results are conducted to show these conclusions.

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.

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