Bounded mismatch model of steering vector error for adaptive array MIMO radar

2009 ◽  
Author(s):  
Bin Xia ◽  
Jia Xu ◽  
Yingning Peng ◽  
Xiutan Wang
Keyword(s):  
Mimo Radar ◽  
Adaptive Array ◽  
Steering Vector ◽  
Vector Error ◽  
Steering Vector Error

scholarly journals Robust Capon Beamforming against Steering Vector Error Dominated by Large Direction-of-Arrival Mismatch for Passive Sonar

2019 ◽  
Vol 7 (3) ◽  
pp. 80 ◽  
Author(s):  
Yu Hao ◽  
Nan Zou ◽  
Guolong Liang
Keyword(s):  
Decomposition Theorem ◽  
Direction Of Arrival ◽  
Superior Performance ◽  
Oblique Projection ◽  
Steering Vector ◽  
Practical Applications ◽  
Passive Sonar ◽  
Vector Error ◽  
Doa Mismatch ◽  
Steering Vector Error

Capon beamforming is often applied in passive sonar to improve the detectability of weak underwater targets. However, we often have no accurate prior information of the direction-of-arrival (DOA) of the target in the practical applications of passive sonar. In this case, Capon beamformer will suffer from performance degradation due to the steering vector error dominated by large DOA mismatch. To solve this, a new robust Capon beamforming approach is proposed. The essence of the proposed method is to decompose the actual steering vector into two components by oblique projection onto a subspace and then estimate the actual steering vector in two steps. First, we estimate the oblique projection steering vector within the subspace by maximizing the output power while controlling the power from the sidelobe region. Subsequently, we search for the actual steering vector within the neighborhood of the estimated oblique projection steering vector by maximizing the output signal-to-interference-plus-noise ratio (SINR). Semidefinite relaxation and Charnes-Cooper transformation are utilized to derive convex formulations of the estimation problems, and the optimal solutions are obtained by the rank-one decomposition theorem. Numerical simulations demonstrate that the proposed method can provide superior performance, as compared with several previously proposed robust Capon beamformers in the presence of large DOA mismatch and other array imperfections.

Robust STAP Algorithm under the Steering Vector Uncertainty Set

2011 ◽  
Vol 403-408 ◽  
pp. 2640-2644
Author(s):  
Dong Xia
Keyword(s):  
Weight Vector ◽  
Optimization Criterion ◽  
Steering Vector ◽  
Vector Error ◽  
Improvement Factor ◽  
Uncertainty Set ◽  
Steering Vector Mismatch ◽  
Space Time Adaptive Processing ◽  
Steering Vector Uncertainty ◽  
Steering Vector Error

For solving the problem of optimum space-time adaptive processing (STAP) under the steering vector mismatch, a robust STAP algorithm is proposed based on the concept of robust Capon beamforming. The model of the steering vector error is established, describing the uncertainty of the desired steering vector. And a new optimization criterion is formed, by which the robust weight vector is acquired. Eventually experimental results and analysis are given with simulation signals. It is verified that the presented method is insensitive to the steering vector error and has a bigger improvement factor.

scholarly journals A Novel Unitary ESPRIT Algorithm for Monostatic FDA-MIMO Radar

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 827 ◽  
Author(s):  
Feilong Liu ◽  
Xianpeng Wang ◽  
Mengxing Huang ◽  
Liangtian Wan ◽  
Huafei Wang ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Estimation Accuracy ◽  
Phase Ambiguity ◽  
Steering Vector ◽  
Input Multiple Output ◽  
Joint Direction ◽  
Esprit Algorithm ◽  
Cramer Rao Bound

A novel unitary estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, for the joint direction of arrival (DOA) and range estimation in a monostatic multiple-input multiple-output (MIMO) radar with a frequency diverse array (FDA), is proposed. Firstly, by utilizing the property of Centro-Hermitian of the received data, the extended real-valued data is constructed to improve estimation accuracy and reduce computational complexity via unitary transformation. Then, to avoid the coupling between the angle and range in the transmitting array steering vector, the DOA is estimated by using the rotation invariance of the receiving subarrays. Thereafter, an automatic pairing method is applied to estimate the range of the target. Since phase ambiguity is caused by the phase periodicity of the transmitting array steering vector, a removal method of phase ambiguity is proposed. Finally, the expression of Cramér–Rao Bound (CRB) is derived and the computational complexity of the proposed algorithm is compared with the ESPRIT algorithm. The effectiveness of the proposed algorithm is verified by simulation results.

Robust Adaptive Beamforming Applied to Planar Array

2013 ◽  
Vol 347-350 ◽  
pp. 3930-3933
Author(s):  
Hai Yan Song ◽  
Jie Shi ◽  
Bo Sheng Liu ◽  
Ye Tian ◽  
Jun Ye
Keyword(s):  
Natural Extension ◽  
Side Lobe ◽  
Adaptive Beamforming ◽  
Interference Rejection ◽  
Vector Error ◽  
Second Order Cone ◽  
Planar Array ◽  
Robust Adaptive ◽  
Steering Vector Error ◽  
Beamforming Algorithm

Capon beamforming has better resolution and interference rejection capability. However, its performance will seriously degrade due to noise, array steering vector error, and other factors. In this paper, a robust Capon beamforming applied to a planar array is described. It is shown that the proposed method is the natural extension of the original Vector Optimization Robust Beamforming algorithm to the case of a planar array, and can be reformulated as a convex second-order cone program and solved by SEDUMI. Computer simulation has shown that the proposed method has better performance than other conventional methods, such as narrower main lobe and lower side lobe.

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