Asymptotic performance of the Low Rank Adaptive Normalized Matched Filter in a large dimensional regime

Abstract : The paper addresses the problem of approximating the detector distribution used in target detection embedded in a disturbance composed of a low rank Gaussian noise and a white Gaussian noise. In this context, it is interesting to use an adaptive version of the Low Rank Normalized Matched Filter (LR-ANMF) detector, which is a function of the estimated projector onto the low rank noise subspace. We will show that the traditional approximation of the LR-ANMF detector distribution is not always the better one. In this paper, we propose to perform its limits when the number of secondary data K and the data dimension m both tend to infinity at the same rate m/K → c ∈ (0, ∞). Then, we give the theoretical distributions of these limits in the large dimensional regime and approximate the LR-ANMF detector distribution by them. The comparison of empirical and theoretical distributions on a jamming application shows the interest of our approach.
Document type :
Conference papers
Complete list of metadatas

Cited literature [13 references]  Display  Hide  Download

http://hal.univ-grenoble-alpes.fr/hal-01226381
Contributor : Guillaume Ginolhac <>
Submitted on : Monday, November 9, 2015 - 2:35:30 PM
Last modification on : Tuesday, May 14, 2019 - 9:36:24 AM
Long-term archiving on : Wednesday, February 10, 2016 - 10:31:32 AM

File

ICASSP2015v2.pdf
Files produced by the author(s)

Identifiers

Citation

Alice Combernoux, Frédéric Pascal, Guillaume Ginolhac, Marc Lesturgie. Asymptotic performance of the Low Rank Adaptive Normalized Matched Filter in a large dimensional regime. 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015), Apr 2015, Brisbane, Australia. pp.2599 - 2603, ⟨10.1109/ICASSP.2015.7178441⟩. ⟨hal-01226381⟩

Share

Metrics

Record views

705

Files downloads

351