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A New Robust Kalman Filter-Based Subspace Tracking Algorithm in an Impulsive Noise Environment
<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>
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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
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... 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).
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