  We propose the adaptive random Fourier features Gaussian kernel LMS
(ARFF-GKLMS). Like most kernel adaptive filters based on stochastic gradient
descent, this algorithm uses a preset number of random Fourier features to save
computation cost. However, as an extra flexibility, it can adapt the inherent
kernel bandwidth in the random Fourier features in an online manner. This
adaptation mechanism allows to alleviate the problem of selecting the kernel
bandwidth beforehand for the benefit of an improved tracking in non-stationary
circumstances. Simulation results confirm that the proposed algorithm achieves
a performance improvement in terms of convergence rate, error at steady-state
and tracking ability over other kernel adaptive filters with preset kernel
bandwidth.
