scholarly journals Adaptive Beamforming for Passive Synthetic Aperture with Uncertain Curvilinear Trajectory

2021 ◽  
Vol 13 (13) ◽  
pp. 2562
Author(s):  
Peng Chen ◽  
Long Zuo ◽  
Wei Wang
Keyword(s):  
Covariance Matrix ◽  
Optimization Method ◽  
Spatial Spectrum ◽  
Adaptive Beamforming ◽  
Synthetic Aperture ◽  
Signal Subspace ◽  
Direction Error ◽  
Sample Covariance ◽  
Noise Covariance ◽  
Curvilinear Trajectory

Recently, numerous reconstruction-based adaptive beamformers have been proposed, which can improve the quality of imaging or localization in the application of passive synthetic aperture (PSA) sensing. However, when the trajectory is curvilinear, existing beamformers may not be robust enough to suppress interferences efficiently. To overcome the model mismatch of unknown curvilinear trajectory, this paper presents an adaptive beamforming algorithm by reconstructing the interference-plus-noise covariance matrix (INCM). Using the idea of signal subspace fitting, we construct a joint optimization problem, where the unknown directions of arrival (DOAs) and array shape parameters are coupled together. To tackle this problem, we develop a hybrid optimization method by combining the genetic algorithm and difference-based quasi-Newton method. Then, a set of non-orthogonal bases for signal subspace is estimated with an acceptable computational complexity. Instead of reconstructing the covariance matrix by integrating the spatial spectrum over interference angular sector, we extract the desired signal covariance matrix (DSCM) directly from signal subspace, and then the INCM is reconstructed by eliminating DSCM from the sample covariance matrix (SCM). Numerical simulations demonstrate the robustness of the proposed beamformer in the case of signal direction error, local scattering and random curvilinear trajectory.

scholarly journals An Improved Sub-Array Adaptive Beamforming Technique Based on Multiple Sources of Errors

2020 ◽  
Vol 8 (10) ◽  
pp. 757
Author(s):  
Zhuang Xie ◽  
Jiahua Zhu ◽  
Chongyi Fan ◽  
Xiaotao Huang
Keyword(s):  
Covariance Matrix ◽  
Correlation Coefficients ◽  
Adaptive Beamforming ◽  
Multiple Sources ◽  
Sample Covariance ◽  
Noise Covariance ◽  
Optimal Weighting ◽  
Robust Adaptive ◽  
Beamforming Technique ◽  
Array Errors

In this paper, a new robust adaptive beamforming method is proposed in order to improve the robustness against steering vector (SV) mismatches that arise from multiple types of array errors. First, the sub-array technique is applied in order to obtain the decoupled sample covariance matrix (DSCM), in which the auxiliary sensors are selected to decouple the array. The decoupled interference-plus-noise covariance matrix (DINCM) is reconstructed with the estimated interference SV and maximum eigenvalue of the DSCM. Furthermore, the desired signal SV is estimated as the corresponding eigenvector determined by the correlation coefficients of the assumed SV and eigenvectors. Finally, the optimal weighting vector is obtained by combining the reconstructed DINCM and the estimated desired signal SV. Our simulation results show significant signal-to-interference-plus-noise ratio (SINR) enhancement of the proposed method over existing methods under multiple types of array errors.

scholarly journals High Performance Robust Adaptive Beamforming in the Presence of Array Imperfections

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Wenxing Li ◽  
Xiaojun Mao ◽  
Zhuqun Zhai ◽  
Yingsong Li
Keyword(s):  
Covariance Matrix ◽  
High Performance ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Adaptive Beamforming ◽  
Steering Vector ◽  
Steering Vector Estimation ◽  
Sample Covariance ◽  
Noise Ratio ◽  
Robust Adaptive

A high performance robust beamforming scheme is proposed to combat model mismatch. Our method lies in the novel construction of interference-plus-noise (IPN) covariance matrix. The IPN covariance matrix consists of two parts. The first part is obtained by utilizing the Capon spectrum estimator integrated over a region separated from the direction of the desired signal and the second part is acquired by removing the desired signal component from the sample covariance matrix. Then a weighted summation of these two parts is utilized to reconstruct the IPN matrix. Moreover, a steering vector estimation method based on orthogonal constraint is also proposed. In this method, the presumed steering vector is corrected via orthogonal constraint under the condition where the estimation does not converge to any of the interference steering vectors. To further improve the proposed method in low signal-to-noise ratio (SNR), a hybrid method is proposed by incorporating the diagonal loading method into the IPN matrix reconstruction. Finally, various simulations are performed to demonstrate that the proposed beamformer provides strong robustness against a variety of array mismatches. The output signal-to-interference-plus-noise ratio (SINR) improvement of the beamformer due to the proposed method is significant.

scholarly journals Low-Complexity Robust Adaptive Beamforming Based on INCM Reconstruction via Subspace Projection

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7783
Author(s):  
Yanliang Duan ◽  
Xinhua Yu ◽  
Lirong Mei ◽  
Weiping Cao
Keyword(s):  
Covariance Matrix ◽  
Minimum Mean Square Error ◽  
Low Complexity ◽  
Training Sequence ◽  
Adaptive Beamforming ◽  
Projection Matrix ◽  
Robust Adaptive Beamforming ◽  
Sample Covariance ◽  
Blocking Matrix ◽  
Robust Adaptive

Adaptive beamforming is sensitive to steering vector (SV) and covariance matrix mismatches, especially when the signal of interest (SOI) component exists in the training sequence. In this paper, we present a low-complexity robust adaptive beamforming (RAB) method based on an interference–noise covariance matrix (INCM) reconstruction and SOI SV estimation. First, the proposed method employs the minimum mean square error criterion to construct the blocking matrix. Then, the projection matrix is obtained by projecting the blocking matrix onto the signal subspace of the sample covariance matrix (SCM). The INCM is reconstructed by replacing part of the eigenvector columns of the SCM with the corresponding eigenvectors of the projection matrix. On the other hand, the SOI SV is estimated via the iterative mismatch approximation method. The proposed method only needs to know the priori-knowledge of the array geometry and angular region where the SOI is located. The simulation results showed that the proposed method can deal with multiple types of mismatches, while taking into account both low complexity and high robustness.

scholarly journals A MIMO-SAR Tomography Algorithm Based on Fully-Polarimetric Data

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4839
Author(s):  
Kong ◽  
Xu
Keyword(s):  
Synthetic Aperture Radar ◽  
Covariance Matrix ◽  
Multiple Input Multiple Output ◽  
Three Dimensional ◽  
Synthetic Aperture ◽  
Estimation Accuracy ◽  
Sample Covariance ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Aperture Radar

A fully-polarimetric unitary multiple signal classification (UMUSIC) tomography algorithm is proposed, which can be used for acquiring high-resolution three-dimensional (3D) imagery, in a polarimetric multiple-input multiple-output synthetic aperture radar (MIMO-SAR) with a small number of baselines. In terms of the elevation resolution, UMUSIC provides an improvement over standard MUSIC by utilizing the conjugate of the complex sample data and converting the complex covariance matrix into a real matrix. The combination of UMUSIC and fully-polarimetric data permits a further reduction of the noise of the sample covariance matrix, which is obtained through pixel averaging of multiple two-dimensional (2D) images. Considering the consistency of four polarizations, this algorithm not only makes scattering centers have the same estimated height in four polarizations, but it also improves the estimation accuracy. Simulation results show that this algorithm outperforms the popular distributed compressed sensing (DCS). Image processing of measured data of an aircraft model using a multiple-input multiple-output synthetic aperture radar (MIMO-SAR) with six baselines is presented to validate the proposed algorithm.

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