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On using a priori knowledge in space-time adaptive processing
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
2008 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, Vol. 56, no 6, 2598-2602 p.Article in journal (Refereed) Published
Abstract [en]

In space-time adaptive processing (STAP), the clutter covariance matrix is routinely estimated from secondary "target-free" data. Because this type of data is, more often than not, rather scarce, the so-obtained estimates of the clutter covariance matrix are typically rather poor. In knowledge-aided (KA) STAP, an a priori guess of the clutter covariance matrix (e.g., derived from knowledge of the terrain probed by the radar) is available. In this note, we describe a computationally simple and fully automatic method for combining this prior guess with secondary data to obtain a theoretically optimal (in the mean-squared error sense) estimate of the clutter covariance matrix. The authors apply the proposed method to the KASSPER data set to illustrate the type of achievable performance.

Place, publisher, year, edition, pages
2008. Vol. 56, no 6, 2598-2602 p.
Keyword [en]
convex combination, general linear combination, knowledge-aided, space-time adaptive processing
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:uu:diva-104404DOI: 10.1109/TSP.2007.914347ISI: 000256153800038OAI: oai:DiVA.org:uu-104404DiVA: diva2:219745
Available from: 2009-05-28 Created: 2009-05-28 Last updated: 2009-11-04Bibliographically approved

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Stoica, Peter

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