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Low-complexity robust adaptive beamforming method for MIMO radar based on covariance matrix estimation and steering vector mismatch correction

机译:基于协方差矩阵估计和转向向量不匹配校正的MIMO雷达的低复杂性鲁棒自适应波束形成方法

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摘要

In this study, the authors consider a low-complexity robust adaptive beamforming problem in a collocated multiple-input multiple-output (MIMO) radar. This study is motivated by the fact that in practical applications, the conventional adaptive beamforming algorithm for MIMO radar requires a large computational complexity and suffers from a great performance degradation because of the finite number of training snapshots, the desired signal steering vector mismatch and the corruption of training data by the desired signal. Since the dimension of the virtual steering vector of the MIMO radar is relatively large, the proposed method can estimate the covariance matrix by using a low-complexity method to effectively improve the computational efficiency of the adaptive beamforming algorithm and the robustness of the beamformer against the covariance matrix uncertainty. Besides, based on the estimated covariance matrix, the proposed method can also correct the desired signal steering vector mismatch to efficiently prevent the desired signal cancellation phenomenon. Simulation results demonstrate that the performance of the proposed method is always close to that of the optimal processing in a wide range of signal-to-noise ratio or of the number of training snapshots.
机译:在这项研究中,作者认为,在并置多输入多输出(MIMO)雷达中考虑低复杂性的自适应波束形成问题。本研究的动力是,在实际应用中,用于MIMO雷达的传统自适应波束形成算法需要大的计算复杂性并且由于有限数量的训练快照,所需的信号转向载体错配和损坏而遭受了很大的性能劣化。通过所需信号训练数据。由于MIMO雷达的虚拟转向向量的尺寸相对较大,所以所提出的方法可以通过使用低复杂性方法来估计协方差矩阵,以有效地提高自适应波束形成算法的计算效率和波束形成器的鲁棒性对抗协方差矩阵不确定性。此外,基于估计的协方差矩阵,所提出的方法还可以校正所需的信号转向载体失配,以有效地防止所需的信号消除现象。模拟结果表明,所提出的方法的性能总是接近广泛的信噪比或训练快照的数量中的最佳处理的性能。

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