Exploiting multiple a priori spectral models for adaptive radar detection. Issue 7 (1st August 2014)

• Record Type: Journal Article
• Authors: Aubry, Augusto; Carotenuto, Vincenzo; De Maio, Antonio; Foglia, Goffredo
• Is Part Of: IET radar, sonar & navigation. Volume 8:Issue 7(2014)
• Page Start: 695
• Page End: 707
• Publication Date: 2014-08-01
• Subjects: adaptive signal detection -- covariance matrices -- interference suppression -- matrix inversion -- maximum likelihood detection -- object detection -- radar detection -- spectral analysis; a priori spectral model -- adaptive radar detection -- environmental heterogeneity -- cell under test -- lower bound -- power spectral density -- white disturbance -- generalised likelihood ratio test‐based detection algorithms -- interference inverse covariance matrix -- PSD -- target detection; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2013.0233
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : This study deals with the problem of adaptive radar detection when a limited number of training data, due to environmental heterogeneity, is present. Suppose that some a priori spectral models for the interference in the cell under test and a lower bound on the power spectral density (PSD) of the white disturbance term are available. Hence, generalised likelihood ratio test‐based detection algorithms have been devised. At the design stage, the basic idea is to model the actual interference inverse covariance as a combination of the available a priori models and to account for the available lower bound on the PSD. At the analysis stage, the capabilities of the new techniques have been shown to detect targets when few training data are available as well as their superiority with respect to conventional adaptive techniques based on the sample covariance matrix.