This paper proposes a new local polynomial modeling based variable forgetting factor (VFF) and variable regularized (VR) projection approximation subspace tracking (PAST) algorithm, which is based on a novel VR-VFF recursive least squares (RLS) algorithm with multiple outputs. The subspace to be estimated is modeled as a local polynomial model so that a new locally optimal forgetting factor (LOFF) can be obtained by minimizing the resulting mean square deviation of the RLS algorithm after using the projection approximation. An l2-regularization term is also incorporated to the LOFF-PAST algorithm to reduce the estimation variance of the subspace during signal fading. The proposed LOFF-VR-PAST algorithm can be implemented by the conventional RLS algorithm as well as the numerically more stable QR decomposition. Applications of the proposed algorithms to subspace-based direction-of-arrival estimation under stationary and nonstationary environments are presented to validate their effectiveness. Simulation results show that the proposed algorithms offer improved performance over the conventional PAST algorithm and a comparable performance to the Kalman filter with variable measurement subspace tracking algorithm, which requires a considerably higher arithmetic complexity. The new LOFF-VR-RLS algorithm may also be applicable to other RLS problems involving multiple outputs.


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    Title :

    A New Local Polynomial Modeling Based Variable Forgetting Factor and Variable Regularized PAST Algorithm for Subspace Tracking


    Contributors:
    Shing-Chow Chan (author) / Hai-Jun Tan (author) / Jian-Qiang Lin (author) / Bin Liao (author)


    Publication date :

    2018-06-01


    Size :

    1090039 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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