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.
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40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015), Apr 2015, Brisbane, Australia. IEEE international conference on Acoustics, Speech and Signal Processing (ICASSP), pp.2599 - 2603, 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015). 〈10.1109/ICASSP.2015.7178441〉
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Dernière modification le : vendredi 9 novembre 2018 - 13:38:02
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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. IEEE international conference on Acoustics, Speech and Signal Processing (ICASSP), pp.2599 - 2603, 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015). 〈10.1109/ICASSP.2015.7178441〉. 〈hal-01226381〉

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