Tracking performance of adaptively biased adaptive filters

Adaptive filters can improve their performance by exploiting the well known tradeoff between bias and variance of the estimated solution. In a previous work, a scheme for adaptively biasing the filter weights was introduced, multiplying the output of a filter of any kind by a shrinking factor a ∈ [0,1]. With an appropriate value a, such a scheme can reduce the steady-state error, especially for low signal-to-noise ra tio (SNR). Here, we extend such analysis for a tracking scenario in which the optimal solution follows a random walk-model. We briefly review a realizable scheme for learning a, based on recently proposed algorithms for adaptive filter combination. Our experiments validate the accurateness of the analysis, and illustrate the performance gains that can be expected from these biased configurations in stationary and tracking scenarios.