Fast iterative subspace algorithms for airborne STAP radar
EURASIP Journal on Applied Signal Processing
Fast subspace tracking algorithm based on the constrained projection approximation
EURASIP Journal on Advances in Signal Processing
The fast recursive row-Householder subspace tracking algorithm
Signal Processing
Robust nonlinear power iteration algorithm for adaptive blind separation of independent signals
Digital Signal Processing
Low complexity DFT-domain noise PSD tracking using high-resolution periodograms
EURASIP Journal on Advances in Signal Processing
A subspace method for the blind identification of multiple time-varying FIR channels
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Journal of Electrical and Computer Engineering
Fast adaptive algorithms for minor component analysis using Householder transformation
Digital Signal Processing
Proceedings of the 21st ACM international conference on Information and knowledge management
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This paper introduces a fast implementation of the power iteration method for subspace tracking, based on an approximation that is less restrictive than the well-known projection approximation. This algorithm, referred to as the fast approximated power iteration (API) method, guarantees the orthonormality of the subspace weighting matrix at each iteration. Moreover, it outperforms many subspace trackers related to the power iteration method, such as PAST, NIC, NP3, and OPAST, while having the same computational complexity. The API method is designed for both exponential windows and sliding windows. Our numerical simulations show that sliding windows offer a faster tracking response to abrupt signal variations.