Degradation of the tracking performance of adaptive filtering algorithms with data correlation

Degradation of the tracking performance of adaptive filtering algorithms with data correlation is analyzed. The analysis is done in the context of the identification of a randomly time varying plant. Five algorithms are considered. They are least mean square, recursive least squares, sign, signed regressor, and sign-sign algorithms. The analysis is done in terms of the steady-state excess mean-square error and the steady-state mean-square weight misalignment. The degradation measure adopted is the ratio of the performance index for correlated data to that for white data