NDLI: Robust ANMF test using Huber's M-estimator

Content Provider	IEEE Xplore Digital Library
Author	Mahot, M. Pascal, F. Forster, P. Ovarlez, J.P.
Copyright Year	2012
Description	Author affiliation: ONERA, DEMR/TSI, Chemin de la Hunière, 91761 Palaiseau, Cedex, France (Ovarlez, J.P.) \|\| SONDRA, Supelec, 3 rue Joliot-Curie, 91190 Gif-sur-Yvette, France (Mahot, M.; Pascal, F.) \|\| SATIE, ENS Cachan, CNRS, UniverSud, 61, Av. du Pdt Wilson, F-94230, France (Forster, P.)
Abstract	In many statistical signal processing applications, the quality of the estimation of parameters of interest plays an important role. We focus in this paper, on the estimation of the covariance matrix. In the classical Gaussian context, the Sample Covariance Matrix (SCM) is the most often used, since it is the Maximum Likelihood estimate. It is easy to manage and has a lot of well-known statistical properties. However it may exhibit poor performance in context of non-Gaussian signals, contaminated or missing data. In that case M-estimators provide a good alternative. In this paper, we extend to the complex data case, a theoretical result already proposed by Tyler in the real data case, deriving the asymptotical distribution of any homogeneous functional of degree 0 of the M-estimates. Then, applying this result to the Adaptive Normalized Matched Filter (ANMF), we obtain a robust ANMF and give the relationship between its Probability of False Alarm (P) and the detection threshold.
Starting Page	373
Ending Page	376
File Size	542882
Page Count	4
File Format	PDF
ISBN	9781467310703
ISSN	2151870X
e-ISBN	9781467310710
DOI	10.1109/SAM.2012.6250514
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Covariance matrix Robustness Context Vectors Detectors Noise
Content Type	Text
Resource Type	Article

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