Title :
Random matrix theory applied to low rank stap detection
Author :
Combernoux, Alice ; Pascal, F. ; Ginolhac, Guillaume ; Lesturgie, Marc
Author_Institution :
SONDRA, Supelec, Gif-sur-Yvette, France
Abstract :
The paper addresses the problem of target detection embedded in a disturbance composed of a low rank Gaussian clutter and a white Gaussian noise. In this context, it is interesting to use an adaptive version of the Low Rank Normalized Matched Filter detector, denoted LR-ANMF, which is a function of the estimation of the projector onto the clutter subspace. In this paper, we show that the LR-ANMF detector based on the sample covariance matrix is consistent when the number of secondary data K tends to infinity for a fixed data dimension m but not consistent when m and K both tend to infinity at the same rate, i.e. m/K → c ∈ (0, ∞). Using the results of random matrix theory, we then propose a new version of the LR-ANMF which is consistent in both cases. The application of our new detector on STAP (Space Time Adaptive Processing) data shows the interest of our approach.
Keywords :
adaptive filters; covariance matrices; matched filters; object detection; signal detection; space-time adaptive processing; LR-ANMF detector; low rank Gaussian clutter; low rank STAP detection; low rank adaptive normalized matched filter detector; random matrix theory; sample covariance matrix; space time adaptive processing data; target detection; white Gaussian noise; Abstracts; Detectors; IP networks; Adaptive Normalized Matched Filter; G-MUSIC estimator; Low rank detection; Random matrix theory; STAP processing;
Conference_Titel :
Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 21st European
Conference_Location :
Marrakech