A new robust Kalman filter-based subspace tracking algorithm in an impulsive noise environment

Authors:
B. Liao;Z. G. Zhang;S. C. Chan
Affiliations:
Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong;Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong;Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong
Venue:
IEEE Transactions on Circuits and Systems II: Express Briefs
Year:
2010

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Abstract

The conventional projection approximation subspace tracking (PAST) algorithm is based on the recursive least-squares algorithm, and its performance will degrade considerably when the subspace rapidly changes and the additive noise is impulsive. This brief proposes a new robust Kalman filter-based subspace tracking algorithm to overcome these two limitations of the PAST algorithm. It is based on a new extension of the adaptive Kalman filter with variable number of measurements (KFVNM) for tracking fast-varying subspace. Furthermore, M-estimation is incorporated into this KFVNM algorithm to combat the adverse effects of impulsive noise. Simulation results show that the robust KFVNM-based subspace tracking algorithm has a better performance than the PAST algorithm for tracking fast-varying subspace and in an impulsive noise environment.