.png){kind=icon}
.png){kind=icon}
Return
Generalized YAST algorithm for signal subspace tracking
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2015.04.025.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper introduces two generalized versions of the Yet Another Subspace Tracker (YAST) algorithm which can estimate and track the principal subspace with low computational load and with reasonable performance. The YAST algorithm relies on an interesting idea of optimally extracting the updated subspace weighting matrix in each step. This optimal extraction, which has a high computational burden, exhibits an incredible convergence rate. However, the fast implementation of this optimal scheme has been restricted to the temporal domain data case in the existing literature. In addition, all the previous versions of the YAST algorithm suffer from numerical problems. In our new subspace trackers, computation reduction of optimal subspace extraction is achieved by an approximation, which generalizes the application of the YAST algorithm to all data cases. Performances of the YAST algorithms proposed in this paper are experimentally seen to be very close to the optimal VAST algorithm. In fact, these algorithms outperform all existing fast subspace trackers. The numerical stability, with respect to orthonormality, is proved for one of the proposed VAST algorithms. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Fast subspace tracking
Numerical stability
Optimal principal subspace
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.