We consider two-dimensional (2-D) nonparametric complex spectral estimation (with its 1-D counterpart as a special case) of data matrices with missing samples occurring in arbitrary patterns. Previously, the missing amplitude and phase estimation-expectation maximization (MAPES-EM) algorithms were developed for the general 1-D missing-data problem and shown to have excellent spectral estimation performance. In this correspondence, we present 2-D extensions of MAPES-EM and develop another 2-D MAPES algorithm, referred to as MAPES-CM, which solves a maximum likelihood problem iteratively via cyclic maximization (CM). Compared with MAPES-EM, MAPES-CM has similar spectral estimation performance but is computationally much more efficient, which is especially important for long data sequences and 2-D applications such as synthetic aperture radar (SAR) imaging.
Two-dimensional nonparametric spectral analysis in missing data case
IEEE Transactions on Aerospace and Electronic Systems ; 43 , 4 ; 1604-1616
2007-10-01
2561241 byte
Article (Journal)
Electronic Resource
English
CORRESPONDENCE - Two-Dimensional Nonparametric Spectral Analysis in Missing Data Case
Online Contents | 2007
|Some examples and problems of application of nonparametric correlation and spectral analysis
Automotive engineering | 1985
|Recovery of missing data via wavelets followed by high-dimensional modeling
American Institute of Physics | 2017
|