An Improved Low Rank Detector in the High Dimensional Regime
Pascal Vallet, Guillaume Ginolhac, Frédéric Pascal, Philippe Forster
Abstract
This paper introduces an improved Low Rank Adaptive Normalized Matched Filter (LR-ANMF) detector in a high dimensional (HD) context where the observation dimension is large and of the same order of magnitude than the sample size. To that end, the statistical analysis of the LR-ANMF, in a context where the target signal is disturbed by a spatially correlated Gaussian clutter and a spatially white Gaussian noise, is addressed. More specifically, the asymptotic distribution under the null hypothesis is derived, in the regime where both the dimension M of the observations and the number N of samples converge to infinity at the same rate and when the clutter covariance matrix has fixed rank K. In particular, it is shown that the LR-ANMF test statistic does not exhibit the CFAR property in the previous asymptotic regime. A correction to the LR-ANMF test is then proposed to ensure the asymptotic CFAR property, providing the improved LR-ANMF, termed as HD-LR-ANMF. Its asymptotic distribution is derived under both the null and the alternative hypotheses. Numerical simulations illustrate the fact that, despite the asymptotic nature of the analysis, the results obtained are accurate for reasonable values of M, N.
BibTeX
@inproceedings{icassp2019_animprovedlowran,
title = {An Improved Low Rank Detector in the High Dimensional Regime},
author = {Pascal Vallet and Guillaume Ginolhac and Frédéric Pascal and Philippe Forster},
booktitle = {ICASSP 2019},
year = {2019}
}