Automatic Kernel Weighting for Multikernel Adaptive Filtering: Multiscale Aspects

Abstract

This paper presents an automatic kernel weighting technique for multikernel adaptive filtering. The full potential of the multikernel adaptive filtering approach can only be achieved when the kernels are weighted appropriately. The proposed technique balances the dominance of the kernels by making the mean eigenvalues of their associated autocorrelation matrices be equal to each other. The overall complexity of the proposed approach is low because the mean eigenvalues can be computed efficiently. The numerical results verify that the proposed technique balances the coefficient updates and yields reasonable performance.