Analytical model for adaptive noise cancellation in a speech signal
ISPRA'08 Proceedings of the 7th WSEAS International Conference on Signal Processing, Robotics and Automation
Fast subspace tracking algorithm based on the constrained projection approximation
EURASIP Journal on Advances in Signal Processing
Adaptive algorithms to track the PARAFAC decomposition of a third-order tensor
IEEE Transactions on Signal Processing
The QS-householder sliding window Bi-SVD subspace tracker
IEEE Transactions on Signal Processing
Robust nonlinear power iteration algorithm for adaptive blind separation of independent signals
Digital Signal Processing
A Fast Algorithm for Updating and Downsizing the Dominant Kernel Principal Components
SIAM Journal on Matrix Analysis and Applications
Hi-index | 35.69 |
The singular value decomposition (SVD) is an important tool for subspace estimation. In adaptive signal processing, we are especially interested in tracking the SVD of a recursively updated data matrix. This paper introduces a new tracking technique that is designed for rectangular sliding window data matrices. This approach, which is derived from the classical bi-orthogonal iteration SVD algorithm, shows excellent performance in the context of frequency estimation. It proves to be very robust to abrupt signal changes, due to the use of a sliding window. Finally, an ultra-fast tracking algorithm with comparable performance is proposed.