scholarly journals Sparse Array Angle Estimation Using Reduced-Dimension ESPRIT-MUSIC in MIMO Radar

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Chaozhu Zhang ◽  
Yucai Pang
Keyword(s):  
Low Cost ◽  
Direction Cosine ◽  
Mimo Radar ◽  
Angle Estimation ◽  
Linear Arrays ◽  
Reduced Dimension ◽  
Esprit Algorithm ◽  
Receive Array ◽  
True Direction ◽  
High Computational Complexity

Sparse linear arrays provide better performance than the filled linear arrays in terms of angle estimation and resolution with reduced size and low cost. However, they are subject to manifold ambiguity. In this paper, both the transmit array and receive array are sparse linear arrays in the bistatic MIMO radar. Firstly, we present an ESPRIT-MUSIC method in which ESPRIT algorithm is used to obtain ambiguous angle estimates. The disambiguation algorithm uses MUSIC-based procedure to identify the true direction cosine estimate from a set of ambiguous candidate estimates. The paired transmit angle and receive angle can be estimated and the manifold ambiguity can be solved. However, the proposed algorithm has high computational complexity due to the requirement of two-dimension search. Further, the Reduced-Dimension ESPRIT-MUSIC (RD-ESPRIT-MUSIC) is proposed to reduce the complexity of the algorithm. And the RD-ESPRIT-MUSIC only demands one-dimension search. Simulation results demonstrate the effectiveness of the method.

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

2014 ◽  
Vol 23 (08) ◽  
pp. 1450106 ◽  
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.

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.

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.

Modified Propagator Method for 2-Dimensional DOA Estimation in Monostatic MIMO Radar with L-Shaped Array

2013 ◽  
Vol 846-847 ◽  
pp. 1171-1175
Author(s):  
Xin Li ◽  
Ding Jie Xu ◽  
Xiao Meng Wang
Keyword(s):  
Doa Estimation ◽  
Mimo Radar ◽  
Complex Matrix ◽  
Linear Arrays ◽  
Complex Eigenvalues ◽  
Propagator Method ◽  
The Matrix ◽  
Esprit Algorithm ◽  
Influence Of Noise ◽  
Better Than

A modified propagator method based on L-shaped array for 2-Dimensional (2-D) direction of arrival (DOA) estimation in monostatic MIMO radar is proposed. A cross-correlation matrix, which can eliminate the influence of noise, is constructed by the received data from the two orthogonal uniform linear arrays (ULAs) at x-axis and z-axis. Then the matrix can be utilized to estimate signal subspace of 2-D DOA through propagator method. At last, the elevation and azimuth angles of the 2-D DOA is automatically paired by the complex eigenvalues of a low-order complex matrix. The 2-D DOA estimation performance of the proposed method is better than conventional propagator method and ESPRIT algorithm. Simulation results verify the effectiveness of the proposed method.

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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