Blind channel estimation of MIMO-OFDM based on FDPM
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Data stream anomaly detection through principal subspace tracking
Proceedings of the 2010 ACM Symposium on Applied Computing
Interference subspace tracking for network interference alignment in cellular systems
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Journal of Electrical and Computer Engineering
Low complexity adaptive algorithms for Principal and Minor Component Analysis
Digital Signal Processing
Rethinking concepts of the dendritic cell algorithm for multiple data stream analysis
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
Tracking time-varying correlated underwater acoustic channels in the signal subspace
Proceedings of the Eighth ACM International Conference on Underwater Networks and Systems
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We consider the problem of adaptive subspace tracking, when the rank of the subspace we seek to estimate is assumed to be known. Starting from the data projection method (DPM), which constitutes a simple and reliable means for adaptively estimating and tracking subspaces, we develop a fast and numerically robust implementation of DPM, which comes at a considerably lower computational cost. Most existing schemes track subspaces corresponding either to the largest or to the smallest singular values, while our DPM version can switch from one subspace type to the other with a simple change of sign of its single parameter. The proposed algorithm provides orthonormal vector estimates of the subspace basis that are numerically stable since they do not accumulate roundoff errors. In fact, our scheme constitutes the first numerically stable, low complexity, algorithm for tracking subspaces corresponding to the smallest singular values. Regarding convergence towards orthonormality our scheme exhibits the fastest speed among all other subspace tracking algorithms of similar complexity.