Misalignment Performance of Selective Tap Adaptive Algorithms for System Identification of Time-Varying Unknown Systems

Selective tap algorithms have been proposed as a means of reducing complexity for adaptive filtering. MMax tap selection has been employed in many algorithms due to its straightforward implementation. This paper formulates the analysis of two MMax-based algorithms under time-varying unknown system conditions as are often found in practical applications. The steady-state misalignment for the MMax normalized least mean square and the MMax recursive least squares algorithms are derived and their performance is compared to that of their respective full-update algorithms. The tradeoff between computational complexity and misalignment performance is also shown for the MMax normalized least mean square case.