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Communication Dans Un Congrès Année : 2013

Random matrix theory applied to low rank stap detection

Résumé

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. 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.
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Dates et versions

hal-00871217 , version 1 (09-10-2013)

Identifiants

  • HAL Id : hal-00871217 , version 1

Citer

Alice Combernoux, Frédéric Pascal, Guillaume Ginolhac, Marc Lesturgie. Random matrix theory applied to low rank stap detection. 21st European Signal Processing Conference (EUSIPCO 2013), Sep 2013, Marrakech, Morocco. pp.1-5. ⟨hal-00871217⟩
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