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Improving the Performance of Cyclic ESPRIT via Real-Valued Decomposition

机译:通过实值分解提高循环ESPRIT的性能

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摘要

By exploiting the real-valued eigendecom-position (EVD) of the centro-Hermitian matrix, a novel Cyclic ESPRIT algorithm with signal selective property is presented by introducing a new forward backward smoothed covariance matrix. Compared with Cyclic ESPRIT algorithm, the proposed approach has a better performance in the presence of multipath propagation. In addition, this approach not only reduces the computational complexity, but also allows to select desired signals and to ignore interferences by exploiting the cyclostationarity property of signals of interest(SOIs). Simulation results that illustrate the performance of this approach in conjunction with Cyclic ESPRIT algorithm are described.
机译:通过利用中心-赫米特矩阵的实值特征分解(EVD),通过引入新的前向后向平滑协方差矩阵,提出了一种具有信号选择特性的新型循环ESPRIT算法。与循环ESPRIT算法相比,该方法在存在多径传播的情况下具有更好的性能。另外,该方法不仅降低了计算复杂度,而且还允许通过利用感兴趣信号(SOI)的循环平稳特性来选择所需信号并忽略干扰。仿真结果说明了该方法结合循环ESPRIT算法的性能。

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