Improving Multikernel Adaptive Filtering With Selective Bias
Magno T. M. Silva, Renato Candido, Jerónimo Arenas-García, Luis Azpicueta-Ruiz. Improving Multikernel Adaptive Filtering With Selective Bias. In: IEEE International Conference on Acoustics, Speech and Signal Processing, 2018, Calgary. Proceedings of ICASSP’2018, 2018.
In this paper, we propose a scheme to simplify the selection of kernel adaptive filters in a multikernel structure. By multiplying the output of each kernel filter by an adaptive biasing factor between zero and one, the degrading effects of poorly adjusted kernel filters can be minimized, increasing the robustness of the multikernel scheme. This approach is able to deal with the lack of the necessary statistical information for an optimal adjustment of the filter and its structure. The advantages of the proposed scheme with respect to other multikernel solutions are checked by means of numerical examples in the context of signal prediction and system identification.
Nonlinear adaptive signal processing, kernel adaptive filtering, cooperative architectures.