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首页> 外文会议>2010 International ITG Workshopn Smart Antennas >Iterative Sequential GSVD (I-S-GSVD) based prewhitening for multidimensional HOSVD based subspace estimation without knowledge of the noise covariance information
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Iterative Sequential GSVD (I-S-GSVD) based prewhitening for multidimensional HOSVD based subspace estimation without knowledge of the noise covariance information

机译：基于迭代顺序GSVD（I-S-GSVD）的预白化，用于基于多维HOSVD的子空间估计，无需了解噪声协方差信息

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摘要

Recently, the Sequential GSVD (S-GSVD) based prewhitening scheme has been proposed to improve R-dimensional subspace-based parameter estimation schemes in the presence of colored noise or interference with Kronecker structure. To apply the S-GSVD, second order statistics of the noise should be estimated, e.g., via samples captured in the absence of the desired signal components. In this contribution, we propose the Iterative Sequential Generalized Singular Value Decomposition (I-S-GSVD) based prewhitening scheme for multidimensional HOSVD based subspace estimation when information about the noise statistics is not available. Even without the availability of samples in the absence of the desired signals components, it is possible to obtain the prewhitening correlation factors and the signal parameters in an iterative way using a deterministic algorithm in combination with the S-GSVD. This combination constitutes our proposed I-S-GSVD. Finally, the I-S-GSVD inherits the computational efficiency from the S-GSVD compared to matrix based prewhitening schemes.
机译：最近，已经提出了基于序列GSVD（S-GSVD）的预白方案，以在存在有色噪声或存在Kronecker结构干扰的情况下改进基于R维子空间的参数估计方案。为了应用S-GSVD，应该例如通过在缺少期望信号分量的情况下捕获的样本来估计噪声的二阶统计量。在此贡献中，我们提出了基于迭代序列广义奇异值分解（I-S-GSVD）的预白化方案，用于基于多维HOSVD的子空间估计（当有关噪声统计信息不可用时）。即使在缺少所需信号分量的情况下也无法获得样本，也可以使用确定性算法结合S-GSVD以迭代方式获得预白化相关因子和信号参数。这种组合构成了我们提出的I-S-GSVD。最后，与基于矩阵的预增白方案相比，I-S-GSVD继承了S-GSVD的计算效率。

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