NDLI: Exploiting multiple a priori spectral models for adaptive radar detection

Content Provider	IET Digital Library
Author	Aubry, Augusto Carotenuto, Vincenzo Maio, Antonio De Foglia, Goffredo
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.
Starting Page	695
Ending Page	707
Page Count	13
ISSN	17518784
Volume Number	8
e-ISSN	17518792
Issue Number	Issue 7, Aug (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/8/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2013.0233
Journal	IET Radar, Sonar & Navigation
Publisher Date	2014-02-12
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	A Priori Spectral Model Adaptive Radar Detection Adaptive Signal Detection Cell under Test Covariance Matrices Electromagnetic Compatibility Environmental Heterogeneity Generalised Likelihood Ratio Test-based Detection Algorithm Interference Interference Inverse Covariance Matrix Interference Suppression Linear Algebra Lower Bound Matrix Inversion Maximum Likelihood Detection Numerical Analysis Object Detection Power Spectral Density PSD Radar Detection Radar Equipment Signal Detection Spectral Analysis System And Application Target Detection White Disturbance
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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