Robust recursive bi-iteration singular value decomposition (SVD) for subspace tracking and adaptive filtering

The recursive bi-iteration singular value decomposition (Bi-SVD), proposed by Strobach (1997), is an efficient and well-structured algorithm for performing subspace tracking. Unfortunately, its performance under an impulse noise environment degrades substantially. In this paper, a new robust-statistics-based bi-iteration SVD algorithm (robust Bi-SVD) is proposed. Simulation results show that the proposed algorithm offers significantly improved robustness against impulse noise than the conventional algorithm with a slight increase in arithmetic complexity. For nominal Gaussian noise, the two algorithms have similar performance.

[1]  B. Ripley,et al.  Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.

[2]  Peter Strobach.  Bi-iteration recursive instrumental variable subspace tracking and adaptive filtering , 1998, IEEE Trans. Signal Process..

[3]  T. Ng,et al.  A recursive least M-estimate (RLM) adaptive filter for robust filtering in impulse noise , 2000, IEEE Signal Processing Letters.

[4]  Tung-Sang Ng,et al.  A robust M-estimate adaptive filter for impulse noise suppression , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[5]  Shing-Chow Chan,et al.  Robust subspace tracking based blind channel identification in impulsive noise environment , 2002, 2002 11th European Signal Processing Conference.

[6]  P. Strobach.  Low-rank adaptive filters , 1996, IEEE Trans. Signal Process..

[7]  S. C. Chan,et al.  Robust M-estimate adaptive filtering , 2001 .

[8]  Tung-Sang Ng,et al.  A robust statistics based adaptive lattice-ladder filter in impulsive noise , 2000, 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353).

[9]  Tung-Sang Ng,et al.  Fast least mean M-estimate algorithms for robust adaptive filtering in impulse noise , 2000, 2000 10th European Signal Processing Conference.

[10]  Ilkka Karasalo,et al.  Estimating the covariance matrix by signal subspace averaging , 1986, IEEE Trans. Acoust. Speech Signal Process..

[11]  K. L. Ho,et al.  A robust subspace tracking algorithm for subspace-based blind multiuser detection in impulsive noise , 2002, 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628).

[12]  Peter Strobach,et al.  Bi-iteration SVD subspace tracking algorithms , 1997, IEEE Trans. Signal Process..

[13]  F. L. Bauer.  Das Verfahren der Treppeniteration und verwandte Verfahren zur Lösung algebraischer Eigenwertprobleme , 1957 .

[14]  Gene H. Golub,et al.  Matrix computations , 1983 .

[15]  Shing-Chow Chan,et al.  Robust subspace tracking in impulsive noise , 2001, ICC 2001. IEEE International Conference on Communications. Conference Record (Cat. No.01CH37240).