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

B. Liao, Z. G. Zhang, S. C. Chan
<span title="">2010</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/c352rf3zizflhbzkvxrawww7mu" style="color: black;">IEEE Transactions on Circuits and Systems - II - Express Briefs</a> </i> &nbsp;
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
more &raquo; ... g 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. Index Terms-Impulsive noise, Kalman filter, Kalman filter with variable number of measurements (KFVNM), least squares, projection approximation subspace tracking (PAST).
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tcsii.2010.2056414">doi:10.1109/tcsii.2010.2056414</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cvufs4ly6bfqtfmbaxecazp6by">fatcat:cvufs4ly6bfqtfmbaxecazp6by</a> </span>
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