Fast adaptive eigenvalue decomposition: a maximum likelihood approach
Signal Processing
On the adaptive linear estimators, using biased Cramér-Rao bound
Signal Processing
Blind reduced-rank MMSE detector for DS-CDMA systems
EURASIP Journal on Applied Signal Processing
D-BLAST OFDM with channel estimation
EURASIP Journal on Applied Signal Processing
The fast householder Bi-SVD subspace tracking algorithm
Signal Processing
The fast recursive row-Householder subspace tracking algorithm
Signal Processing
Consistent reduced-rank LMMSE estimation with a limited number of samples per observation dimension
IEEE Transactions on Signal Processing
IEEE Transactions on Wireless Communications
The QS-householder sliding window Bi-SVD subspace tracker
IEEE Transactions on Signal Processing
Data stream anomaly detection through principal subspace tracking
Proceedings of the 2010 ACM Symposium on Applied Computing
Sinusoidal order estimation using angles between subspaces
EURASIP Journal on Advances in Signal Processing
Journal of Electrical and Computer Engineering
An incremental updating method for clustering-based high-dimensional data indexing
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Tracking time-varying correlated underwater acoustic channels in the signal subspace
Proceedings of the Eighth ACM International Conference on Underwater Networks and Systems
Hi-index | 35.69 |
We introduce a class of adaptive filters based on sequential adaptive eigendecomposition (subspace tracking) of the data covariance matrix. These new algorithms are completely rank revealing, and hence, they can perfectly handle the following two relevant data cases where conventional recursive least squares (RLS) methods fail to provide satisfactory results: (1) highly oversampled “smooth” data with rank deficient of almost rank deficient covariance matrix and (2) noise-corrupted data where a signal must be separated effectively from superimposed noise. This paper contradicts the widely held belief that rank revealing algorithms must be computationally more demanding than conventional recursive least squares. A spatial RLS adaptive filter has a complexity of O(N2) operations per time step, where N is the filter order. The corresponding low-rank adaptive filter requires only O(Nr) operations per time step, where r⩽N denotes the rank of the data covariance matrix. Thus, low-rank adaptive filters can be computationally less (or even much less) demanding, depending on the order/rank ratio N/r or the compressibility of the signal. Simulation results substantiate our claims. This paper is devoted to the theory and application of fast orthogonal iteration and bi-iteration subspace tracking algorithms