NDLI: Median matrices and geometric barycenters for training data selection

Content Provider	IEEE Xplore Digital Library
Author	Aubry, A. De Maio, A. Pallotta, L. Farina, A. Fantacci, C.
Copyright Year	2013
Description	Author affiliation: SELEX Electron. Syst., Rome, Italy (Farina, A.) \|\| DIETI, Univ. degli Studi di Napoli “Federico II”, Naples, Italy (De Maio, A.; Pallotta, L.) \|\| Univ. di Firenze, Florence, Italy (Fantacci, C.) \|\| IREA, Naples, Italy (Aubry, A.)
Abstract	This paper deals with the problem of covariance matrix estimation for radar signal processing applications. We propose and analyze a class of estimators which do not require any knowledge about the probability distribution of the sample support and exploit the characteristics of the positive definite matrix space. Any estimator of the class is associated with a suitable distance in the considered space and is defined as the median matrix of some basic covariance matrix estimates obtained from the available secondary data set. Then, we apply the new devised estimators to the problem of secondary data selection and compare their performances with those obtained using geometric barycenters.
Sponsorship	IEEE Aerosp. Electron. Syst. Soc.
Starting Page	331
Ending Page	336
File Size	271822
Page Count	6
File Format	PDF
ISBN	9781467348218
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-19
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	German Inst of Navigation
Subject Keyword	Training data Silicon Vectors Probability distribution Electronic mail Covariance matrices Clutter
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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