Robust Adaptive Beamforming Based on Covariance Matrix Reconstruction with Uncertainties
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摘要: 针对有幅相误差的互质阵列，提出了一种基于协方差矩阵重构的鲁棒自适应波束形成方法。该方法的主要思想是重构信号的协方差矩阵。如果幅相误差存在且无法被忽视，协方差矩阵重构的精度会受到幅相误差的影响。为了消除幅相误差的影响，准确地重构信号的协方差矩阵，提出了一种基于最小二乘(TLS)的方法。首先，建立了含有幅相误差的协方差矩阵重构的基本模型。然后，将问题转化为变量误差(EIV)模型。再将幅相误差的校准转换为与幅相误差相关的误差矩阵的估计，再利用估计结果得到信号协方差矩阵的有效估计。为了解决误差矩阵估计问题，提出了一种交替下降算法。仿真结果表明，即使在存在幅相误差的情况下，该方法仍能提高协方差矩阵的重建精度，并且自适应波束的性能优于现有算法。Abstract: A robust adaptive beamforming method based on covariance matrix reconstruction for a coprime array with gain/phase uncertainties is proposed. The main idea of this method is to reconstruct the covariance matrix of the signals. However, the accuracy of the reconstruction of the covariance matrix might be influenced by the gain/phase uncertainties . To eliminate the influence of the gain/phase uncertainties and reconstruct accurately the covariance matrix of the signals, a Total Least Squares (TLS) based method is proposed. First, the basic model of the covariance matrix reconstruction with gain/phase uncertainties is established. Then, the problem is converted into an Errors In Variables (EIV) model. The calibration of the gain/phase uncertainties is then converted into the estimation of an error matrix related to the gain/phase uncertainties. An alternating descent algorithm is developed to solve this problem. Simulation results showed that the proposed method can improve the accuracy of the reconstruction of the covariance matrix and is effective for adaptive beamforming.
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Key words:
- Adaptive beamforming /
- Compressed sensing /
- Gain/phase uncertainties /
- Coprime array
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