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Spatial averaging of time-frequency distributions for signal recovery in uniform linear arrays

机译:均匀线性阵列中信号恢复的时频分布的空间平均

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This paper presents a new approach based on spatial time-frequency averaging for separating signals received by a uniform linear antenna array. In this approach, spatial averaging of the time-frequency distributions (TFDs) of the sensor data is performed at multiple time-frequency points. This averaging restores the diagonal structure of the source TFD matrix necessary for source separation. With spatial averaging, cross-terms move from their off-diagonal positions in the source TFD matrix to become part of the matrix diagonal entries. It is shown that the proposed approach yields improved performance over the case when no spatial averaging is performed. Further, we demonstrate that in the context of source separation, the spatially averaged Wigner-Ville distribution outperforms the combined spatial-time-frequency averaged distributions, such as the one obtained by using the Choi-Williams (1989) distribution. Simulation examples involving the separation of two sources with close AM and FM modulations are presented.
机译:本文提出了一种基于空间时频平均的新方法,用于分离均匀线性天线阵列接收的信号。在这种方法中,在多个时频点上对传感器数据的时频分布(TFD)进行空间平均。该平均恢复了源分离所必需的源TFD矩阵的对角线结构。通过空间平均,交叉项从其在源TFD矩阵中的非对角位置移动,成为矩阵对角项的一部分。结果表明,与不进行空间平均的情况相比,所提出的方法可以提高性能。此外,我们证明了在源分离的情况下,空间平均的Wigner-Ville分布优于组合的时空频率平均分布,例如使用Choi-Williams(1989)分布获得的分布。给出了包括分离两个具有接近AM和FM调制源的仿真示例。

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