scholarly journals Angle Estimation Using Local Searching for Bistatic MIMO Radar with Unknown MCM

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Chaochen Tang ◽  
Hongbing Qiu ◽  
Xin Liu ◽  
Qinghua Tang
Keyword(s):  
Computational Complexity ◽  
Local Search ◽  
Mutual Coupling ◽  
Computational Cost ◽  
Estimation Method ◽  
Mimo Radar ◽  
Joint Estimation ◽  
Search Range ◽  
Angle Estimation ◽  
Multiple Input

Multiple input and multiple output (MIMO) radar systems have advantages over traditional phased-array radar in resolution, parameter identifiability, and target detection. However, the estimation performance of the direction of arrivals (DOAs) and the direction of departures (DODs) will be significantly degraded for a colocated MIMO radar system with unknown mutual coupling matrix (MCM). Although auxiliary sensors (AS) can be set to solve this problem, the computational cost of two-dimensional multiple signal classification (2D-MUSIC) is still large. In this paper, a new angle estimation method is proposed to reduce the computational complexity. First, a local-search range is defined for each initial angle estimation obtained by the MUSIC with AS method. Second, the new estimation of DOAs and DODs of the targets is estimated via the joint estimation theory of angle and mutual coupling coefficient in the local search area. Simulation results validate that the proposed method can obtain the same precision and have the advantage over the global searching in computational complexity.

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 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.

A New Central Angle Estimation Method of Bistatic MIMO Radar

2013 ◽  
Vol 846-847 ◽  
pp. 1197-1200
Author(s):  
Jian Gong ◽  
Yi Duo Guo ◽  
Hui Xie ◽  
Zhi Long Li
Keyword(s):  
Angular Distribution ◽  
Hadamard Product ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Mimo Radar ◽  
Distribution Functions ◽  
Central Angle ◽  
Angle Estimation ◽  
Bistatic Mimo Radar ◽  
Simulation Results

Based on the theory that the steering vectors of coherently distributed targets for bistatic MIMO radar possess a property called Hadamard product rotational invariance (HPRI), a novel central angle estimation algorithm is derived. The proposed algorithm has the advantage of no searching and easily pair the estimated angles. Furthermore, it can deal with different coherently distributed targets with different angular distribution functions or unknown angular distribution functions. Simulation results show the proposed method is effective.

A Space Time Autoaggressive Method Based on Parameters Estimation for Airborne MIMO Radar

2014 ◽  
Vol 556-562 ◽  
pp. 4496-4500
Author(s):  
Xing Hui Chen ◽  
Shi Qiao Gao
Keyword(s):  
Estimation Method ◽  
Mimo Radar ◽  
Space Time ◽  
Parameters Estimation ◽  
Ar Model ◽  
Model Parameters ◽  
Clutter Suppression ◽  
Training Samples ◽  
Multiple Input ◽  
Space Time Adaptive Processing

The clutter distribution of an airborne multiple input and multiple output (MIMO) radar in non-homogeneous environment varies with ranges and samples in different range gates are not independent identically distributed vectors, so that the statistical space time adaptive processing (STAP) methods degrade heavily. A clutter suppression method for airborne MIMO radar in non-homogeneous environments is studied in this paper. Firstly, Space time autoaggressive (STAR) method is introduced to airborne MIMO radar for clutter suppression and then an AR model parameters estimation method for STAR is proposed to decrease the complexity of traditional method. Simulation results show the proposed method can estimate parameters exactly and rapidly with only few training samples and be fit for clutter suppression in non-homogeneous environments.

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.

Tensor-based real-valued subspace approach for angle estimation in bistatic MIMO radar with unknown mutual coupling

2015 ◽  
Vol 116 ◽  
pp. 152-158 ◽  
Author(s):  
Xianpeng Wang ◽  
Wei Wang ◽  
Jing Liu ◽  
Qi Liu ◽  
Ben Wang
Keyword(s):  
Mutual Coupling ◽  
Mimo Radar ◽  
Angle Estimation ◽  
Bistatic Mimo Radar

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

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
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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