Median matrices and their application to radar training data selection. Issue 4 (1st April 2014)

• Record Type: Journal Article
• Authors: Aubry, Augusto; De Maio, Antonio; Pallotta, Luca; Farina, Alfonso
• Is Part Of: IET radar, sonar & navigation. Volume 8:Issue 4(2014)
• Page Start: 265
• Page End: 274
• Publication Date: 2014-04-01
• Subjects: computational complexity -- covariance matrices -- data analysis -- estimation theory -- probability -- radar signal processing; median matrices -- radar training data selection -- covariance matrix estimation -- radar signal processing applications -- probability distribution -- positive definite matrix space -- secondary data selection -- computational complexity; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2013.0043
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : This study deals with the problem of covariance matrix estimation for radar signal processing applications. The authors propose and analyse 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, the new devised estimators are applied to the problem of secondary data selection and their performances are compared with those obtained using geometric barycenters. The results show that data selectors exploiting geometric medians can outperform those based on geometric barycenters but the former requires a computational complexity higher than the latter.