Block-recursive IAA-based spectral estimates with missing samples using data interpolation
George-Othon Glentis, Andreas Jakobsson, K. Angelopoulos · 2014
In this work, we examine a computationally efficient block-updating scheme for estimating the spectral content of signals with missing samples. The work is an extension of our recent single-sample data interpolation updating of the Iterative Adaptive Approach (IAA), being reformulated to incorporate blocks of samples. The proposed implementation offers a substantial complexity reduction as compared to earlier presented updating schemes, without sacrificing the quality of the resulting spectral estimates more than marginally (if at all).