A Sliding-Window Kernel RLS Algorithm and its Application to Nonlinear Channel Identification
| Title | A Sliding-Window Kernel RLS Algorithm and its Application to Nonlinear Channel Identification |
| Publication Type | Conference Paper |
| Year of Publication | 2006 |
| Authors | Van Vaerenbergh, S., J. Vía, and I. Santamaría |
| Conference Name | IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006) |
| Month Published | May |
| Conference Location | Toulouse, France |
| Keywords | kernel adaptive filtering, KRLS |
| Abstract | In this paper we propose a new kernel-based version of the recursive least-squares (RLS) algorithm for fast adaptive nonlinear filtering. Unlike other previous approaches, we combine a sliding-window approach (to fix the dimensions of the kernel matrix) with conventional -norm regularization (to improve generalization). The proposed kernel RLS algorithm is applied to a nonlinear channel identification problem (specifically, a linear filter followed by a memoryless nonlinearity), which typically appears in satellite communications or digital magnetic recording systems. We show that the proposed algorithm is able to operate in a time-varying environment and tracks abrupt changes in either the linear filter or the nonlinearity. |
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