Paper Info
Title
Knowledge-Aided STAP in Heterogeneous Clutter using a Hierarchical Bayesian Algorithm
Abstract
The problem of estimating the covariance matrix of a primary vector from heterogeneous samples and some prior knowledge is addressed, under the framework of knowledge-aided space-time adaptive processing (KA-STAP). More precisely, a Gaussian scenario is considered where the covariance matrix of the secondary data may differ from the one of interest. Additionally, some knowledge on the primary data is supposed to be available and summarized in a prior matrix. Two KA-estimation schemes are presented in a Bayesian framework whereby the minimum mean square error (MMSE) estimates are derived. The first scheme is an extension of a previous work and takes into account the nonhomogeneity via an original relation. In search of simplicity and to reduce the computational load, a second estimation scheme, less complex, is proposed and omits the fact that the environment may be heterogeneous. Along the estimation process, not only the covariance matrix is estimated but also some parameters representing the degree of a priori and/or the degree of heterogeneity. Performance of the two approaches are then compared using STAP synthetic data. STAP filter shapes are analyzed and also compared with a colored loading technique.
Year
DOI
Venue
2011
10.1109/TAES.2011.5937270
IEEE Trans. Aerospace and Electronic Systems
Keywords
Field
DocType
Covariance matrix,Estimation,Clutter,Bayesian methods,Data models,Noise,Robustness
Estimation of covariance matrices,Bayesian inference,Pattern recognition,Matrix (mathematics),Minimum mean square error,Synthetic data,Artificial intelligence,Gaussian process,Covariance matrix,Mathematics,Space-time adaptive processing
Journal
Volume
Issue
ISSN
47
3
0018-9251
Citations 
PageRank 
References 
13
0.62
12
Authors
3
Name
Order
Citations
PageRank
S. Bidon11176.07
Olivier Besson261065.49
Jean-Yves Tourneret383564.32