Adaptively Biasing the Weights of Adaptive Filters Articles uri icon

publication date

  • July 2010

start page

  • 3890

end page

  • 3895

issue

  • 7

volume

  • 58

international standard serial number (ISSN)

  • 1053-587X

electronic international standard serial number (EISSN)

  • 1941-0476

abstract

  • It is a well-known result of estimation theory that biased estimators can outperform unbiased ones in terms of expected quadratic error. In steady state, many adaptive filtering algorithms offer an unbiased
    estimation of both the reference signal and the unknown true parameter
    vector. In this correspondence, we propose a simple yet effective scheme
    for adaptively biasing the weights of adaptive filters using an output
    multiplicative factor. We give theoretical results that show that the
    proposed configuration is able to provide a convenient bias versus
    variance tradeoff, leading to reductions in the filter mean-square
    error, especially in situations with a low signal-to-noise ratio (SNR).
    After reinterpreting the biased estimator as the combination of the
    original filter and a filter with constant output equal to 0, we propose
    practical schemes to adaptively adjust the multiplicative factor.
    Experiments are carried out for the normalized least-mean-squares (NLMS)
    adaptive filter, improving its mean-square performance in stationary
    situations and during the convergence phase.