Paper Info
Title
An Information-Theoretic View Of Array Processing
Abstract
The removal of noise and interference from an array of received signals is a most fundamental problem in signal processing research. To date, many well-known solutions based on second-order statistics (SOS) have been proposed. This paper views the signal enhancement problem as one of maximizing the mutual information between the source signal and array output. It is shown that if the signal and noise are Gaussian, the maximum mutual information estimation (MMIE) solution is not unique but consists of an infinite set of solutions which encompass the SOS-based optimal filters. The application of the MMIE principle to Laplacian signals is then examined by considering the important problem of estimating a speech signal from a set of noisy observations. It is revealed that while speech (well modeled by a Laplacian distribution) possesses higher order statistics (HOS), the well-known SOS-based optimal filters maximize the Laplacian mutual information as well; that is, the Laplacian mutual information differs from the Gaussian mutual information by a single term whose dependence on the beamforming weights is negligible. Simulation results verify these findings.
Year
DOI
Venue
2009
10.1109/TASL.2008.2010277
IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
Keywords
DocType
Volume
Array signal processing, beamforming, information entropy, mutual information
Journal
17
Issue
ISSN
Citations 
2
1558-7916
1
PageRank 
References 
Authors
0.48
8
3
Name
Order
Citations
PageRank
Jacek Dmochowski1746.52
Jacob Benesty21386136.42
Sofiène Affes396297.26