Adaptive-FRESH Filters for Compensation of Cycle-Frequency Errors

This paper deals with the problem of uncertainties in the periodicities of linear almost-periodically time-variant (LAPTV) filters. These filters are usually implemented as a set of branches, each consisting of a frequency shifter followed by a linear time-invariant (LTI) filter. This implementation is also known as FRESH filters. This paper is motivated by the fact that, when there exist errors in the frequency shifts, the optimum set of LTI filters is obtained by canceling the outputs of the corresponding branches. The purpose of this paper is to analyze the nonstationary behavior of adaptive filters in order to mitigate this problem. Our results show that an adaptive filter can offset the errors in the frequency shifts. The reason is that the coefficients of the adaptive filter are updated so that the filter actually performs as a linear periodically time-variant filter for each branch. This allows to track the errors in the frequency shifts when the rate of convergence of the adaptive algorithm is suitably selected. An analytical study of the convergence is presented, which allows to compute the optimal rate of convergence and the mean squared-error attained by the adaptive filter.