Robust ANMF Detection in Noncentered Impulsive Background - Archive ouverte HAL Access content directly
Journal Articles IEEE Signal Processing Letters Year : 2017

Robust ANMF Detection in Noncentered Impulsive Background

(1) , (2, 1) , (3)
1
2
3

Abstract

One of the most general and acknowledged models for background statistics characterization is the family of elliptically symmetric distributions. They account for heterogeneity and non-Gaussianity of real data. Today, although nonGaussian models are assumed for background modeling and design of detectors, the parameters estimation is usually performed using classical Gaussian-based estimators. This letter analyzes robust estimation techniques in a nonGaussian environment and highlights their interest as an alternative to classical procedures for target detection purposes. The goal of this letter is to extend well-known detection methodologies to nonGaussian framework, when the statistical mean is nonnull and unknown. Furthermore, a theoretical closed-form expression for false-alarm regulation is derived and the Constant False Alarm Rate property is pursued to allow the detector to be independent of nuisance parameters. The experimental validation is conducted on simulations.
Fichier principal
Vignette du fichier
Robust_Target_v4.pdf (313.68 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01692403 , version 1 (26-02-2020)

Identifiers

Cite

Joana Frontera-Pons, Jean-Philippe Ovarlez, Frederic Pascal. Robust ANMF Detection in Noncentered Impulsive Background. IEEE Signal Processing Letters, 2017, 24 (12), pp.1891 - 1895. ⟨10.1109/LSP.2017.2763784⟩. ⟨hal-01692403⟩
121 View
80 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More