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A New Robust Kalman Filter-Based Subspace Tracking Algorithm in an Impulsive Noise Environment

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3 Author(s)
Liao, B. ; Dept. of Electr. & Electron. Eng., Univ. of Hong Kong, Hong Kong, China ; Zhang, Z.G. ; Chan, S.C.

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.

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Circuits and Systems II: Express Briefs, IEEE Transactions on  (Volume:57 ,  Issue: 9 )