PERFORMANCE ANALYSIS OF A LOW-RANK DETECTOR UNDER TRAINING DATA CONTAMINATION - Université Grenoble Alpes Access content directly
Conference Papers Year :

PERFORMANCE ANALYSIS OF A LOW-RANK DETECTOR UNDER TRAINING DATA CONTAMINATION

Abstract

We consider the problem of detecting a known M-dimensional target signature vector from an observation corrupted by an additive noise with unknown covariance matrix. In that case, standard statistical methods of detection usually assume that N "target free" observations are available to perform estimation of the noise covariance matrix. However, in several applications, the target signal may contaminate the training data, resulting in a deviation of the expected performance of the detectors. In this paper, we consider the performance analysis of two low-rank detectors under the assumption that Nc elements of the training data are contaminated by the target signal. More precisely, we derive the asymptotic false alarm and detection probabilities in the high dimensional regime in which both the dimension M , the number of training data N and contaminated data Nc converge to infinity at the same rate. Numerical simulations illustrate the fact that, despite the asymptotic nature of the analysis, the results obtained are accurate for reasonable values of M , N and Nc.
Fichier principal
Vignette du fichier
camsap19.pdf (360.1 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02388669 , version 1 (02-12-2019)

Identifiers

  • HAL Id : hal-02388669 , version 1

Cite

Pascal Vallet, Guillaume Ginolhac, Frederic Pascal, P Forster. PERFORMANCE ANALYSIS OF A LOW-RANK DETECTOR UNDER TRAINING DATA CONTAMINATION. IEEE CAMSAP 2019, IEEE, Dec 2019, Gosier, France. ⟨hal-02388669⟩
74 View
151 Download

Share

Gmail Facebook Twitter LinkedIn More