On Data-Selective Adaptive Filtering

The current trend of acquiring data pervasively calls for some data-selection strategy, particularly in the case a subset of the data does not bring enough innovation. In this paper, we present some extensions of the existing adaptive filtering algorithms enabling data selection, which also address the censorship of outliers measured through unexpected high estimation errors. The resulting algorithms allow the prescription of how often the acquired data are expected to be incorporated in the learning process based on some a priori assumptions regarding the environment data. A detailed derivation of how to implement the data selection in a computationally efficient way is provided along with the proper choice of the parameters inherent to the data-selective affine projection algorithms. Similar discussions lead to the proposal of the data-selective least mean square and data-selective recursive least squares algorithms. Simulation results show the effectiveness of the proposed algorithms for selecting the innovative data without sacrificing the estimation accuracy, while reducing the computational cost.