Journal Article

Exploiting multiple a priori spectral models for adaptive radar detection

Carotenuto, Vincenzo, Foglia, Goffredo, Aubry, Augusto, De Maio, Antonio

2014

Overview

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.

• Publisher: The Institution of Engineering and Technology,The Institution of Engineering & Technology
• Subject: a priori spectral model / adaptive radar detection / adaptive signal detection / Algorithms / cell under test / covariance matrices / Density / environmental heterogeneity / generalised likelihood ratio test‐based detection algorithms / Heterogeneity / Interference / interference inverse covariance matrix / interference suppression / lower bound / Lower bounds / matrix inversion / maximum likelihood detection / object detection / power spectral density / PSD / Radar detection / Spectra / spectral analysis / target detection / Training / white disturbance