High-dimensional sparse covariance estimation for random signals
Document Type
Conference Proceeding
Publication Date
10-18-2013
Abstract
This paper considers the problem of covariance matrix estimation from the viewpoint of statistical signal processing for high-dimensional or wideband random processes. Due to limited sensing resources, it is often desired to accurately estimate the covariance matrix from a small number of sample observations. To make up for the lack of observations, this paper leverages the structural characteristics of the random processes by considering the interplay of three widely-available signal structures: stationarity, sparsity and the underlying probability distribution of the observed random signal. New problem formulations are developed that incorporate both compressive sampling and sparse covariance estimation strategies. Tradeoff study is provided to illustrate the design choices when estimating the covariance matrices using a handful of sample observations. © 2013 IEEE.
Publication Title
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Recommended Citation
Nasif, A.,
Tian, Z.,
&
Ling, Q.
(2013).
High-dimensional sparse covariance estimation for random signals.
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 4658-4662.
http://doi.org/10.1109/ICASSP.2013.6638543
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/10541