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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矢量失配校正的MIMO雷达低复杂度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,作者考虑了并置多输入多输出(MIMO)雷达中的低复杂度鲁棒自适应波束成形问题。这项研究的动机是,在实际应用中,常规的MIMO雷达自适应波束成形算法需要大量的计算复杂性,并且由于训练快照的数量有限,所需的信号转向矢量失配和破坏而导致性能大幅下降。所需信号训练数据。由于MIMO雷达的虚拟导引向量的维数较大,因此该方法可以通过使用低复杂度的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以及波束形成器针对噪声的鲁棒性。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期望的信号导引矢量失配以有效地防止期望的信号消除现象。仿真结果表明,该方法在较大的信噪比或训练快照数量的范围内,其性能始终接近最优处理的性能。

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