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Stable Subspace Tracking Algorithm Based on a Signed URV Decomposition

机译:基于签名URV分解的稳定子空间跟踪算法

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摘要

Subspace estimation and tracking are of fundamental importance in many signal processing algorithms. The class of “Schur subspace estimators” provides a complete parametrization of all “principal subspace estimates,” defined as the column spans of corresponding low-rank matrix approximants that lie within a specified 2-norm distance of a given matrix. The parametrization is found in terms of a two-sided hyperbolic decomposition (Hyperbolic URV, or HURV), which can be computed using hyperbolic rotations. Unfortunately, such rotations are commonly associated with numerical instabilities.
机译:子空间估计和跟踪在许多信号处理算法中至关重要。 “ Schur子空间估计量”类提供了所有“主要子空间估计量”的完整参数化,定义为位于给定矩阵的指定2-范数距离内的相应低秩矩阵近似值的列跨度。根据双面双曲线分解(双曲线URV或HURV)发现参数化,可以使用双曲线旋转来计算。不幸的是,这种旋转通常与数值不稳定性有关。

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