Robust beamforming with imprecise array geometry using steering vector estimation and interference covariance matrix reconstruction
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- 作者：Jie Yang ; Guisheng Liao ; Jun Li ; Yu Lei…
- 关键词：Robust adaptive beamforming ; Steering vector estimation ; Covariance matrix reconstruction ; Worst ; case performance optimization ; Lagrange multiplier methodology
- 刊名：Multidimensional Systems and Signal Processing
- 出版年：2017
- 出版时间：April 2017
- 年：2017
- 卷：28
- 期：2
- 页码：451-469
- 全文大小：
- 刊物类别：Engineering
- 刊物主题：Circuits and Systems; Electrical Engineering; Signal,Image and Speech Processing; Artificial Intelligence (incl. Robotics);
- 出版者：Springer US
- ISSN：1573-0824
- 卷排序：28
文摘
Compared with state-of-the-art robust adaptive beamforming methods, the recently developed steering vector estimation-based beamformer and interference-plus-noise covariance matrix reconstruction-based beamformer are known to provide better robustness against model mismatch. However, both methods may suffer from performance degradation in presence of antenna array geometry error. To alleviate this problem, a novel algorithm is proposed in this paper. In contrast to previous works, the true desired signal steering vector is estimated by solving a new optimization problem, the objective function of which is minimizing the beamformer sensitivity, while the constraint of which is obtained by optimizing the worst-case performance of the constraint adopted in conventional steering vector estimation-based method. We show that the solution of the newly constructed optimization problem can be obtained in closed form by using the Lagrange multiplier methodology, which has a comparable computational complexity with that of the standard Capon beamformer. Subsequently, the precise interference-plus-noise covariance matrix is reconstructed by eliminating the estimated desired signal component from the sample covariance matrix, which mitigates computational burden and accumulated errors induced by the integration process employed in the conventional covariance matrix reconstruction-based method. Owing to the high accuracy of estimated and reconstructed results, the proposed robust adaptive beamforming algorithm can maintain satisfactory performance in cases with imprecise array geometry. Simulation results verify that the proposed method outperforms the existing ones in many scenarios.