Knowledge-Aided STAP in Heterogeneous Clutter using a Hierarchical Bayesian Algorithm

• Published in: IEEE transactions on aerospace and electronic systems Vol. 47; no. 3; pp. 1863 - 1879
• Main Authors: Bidon, S., Besson, O., Tourneret, Jean-Yves
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2011; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Aircraft components; Bayesian analysis; Bayesian methods; Clutter; Computer Science; Covariance matrix; Data models; Electronic systems; Estimation; Gaussian; Heterogeneity; Mathematical analysis; Mean square errors; Noise; Robustness; Signal and Image Processing; Studies; Non-homogeneous environment; Space-time adaptive processing; Bayesian inference; Knowledge-aided; Covariance matrices; Estimation
• ISSN: 0018-9251; 1557-9603; 2371-9877; 1557-9603
• DOI: 10.1109/TAES.2011.5937270

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.