Nonparametric spectral analysis with missing data via the EM algorithm
Jian Li, Yanwei Wang, Petre Stoica, Thomas L. Marzetta · 2005
We consider nonparametric complex spectral estimation of data sequences with missing samples occurring in arbitrary patterns. Several nonparametric algorithms have recently been developed to deal with the missing-data problem. They include, for example, GAPES for gapped data and PG-APES, PG-CAPON for periodically gapped data. However, they are not really suitable for the general missing-data problem where the missing data samples occur in arbitrary patterns. In this paper, we deal with a general missing-data spectral estimation problem for which we develop two nonparametric missing-data amplitude and phase estimation (MAPES) algorithms, both of which make use of the expectation maximization (EM) algorithm. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms.