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dc.contributor.authorKammoun, Abla
dc.contributor.authorCouillet, Romain
dc.contributor.authorPascal, Frederic
dc.contributor.authorAlouini, Mohamed-Slim
dc.date.accessioned2017-11-02T09:09:33Z
dc.date.available2017-11-02T09:09:33Z
dc.date.issued2017-10-25
dc.identifier.citationKammoun A, Couillet R, Pascal F, Alouini M-S (2017) Optimal Design of the Adaptive Normalized Matched Filter Detector using Regularized Tyler Estimators. IEEE Transactions on Aerospace and Electronic Systems: 1–1. Available: http://dx.doi.org/10.1109/taes.2017.2766538.
dc.identifier.issn0018-9251
dc.identifier.doi10.1109/taes.2017.2766538
dc.identifier.urihttp://hdl.handle.net/10754/626097
dc.description.abstractThis article addresses improvements on the design of the adaptive normalized matched filter (ANMF) for radar detection. It is well-acknowledged that the estimation of the noise-clutter covariance matrix is a fundamental step in adaptive radar detection. In this paper, we consider regularized estimation methods which force by construction the eigenvalues of the covariance estimates to be greater than a positive regularization parameter ρ. This makes them more suitable for high dimensional problems with a limited number of secondary data samples than traditional sample covariance estimates. The motivation behind this work is to understand the effect and properly set the value of ρthat improves estimate conditioning while maintaining a low estimation bias. More specifically, we consider the design of the ANMF detector for two kinds of regularized estimators, namely the regularized sample covariance matrix (RSCM), the regularized Tyler estimator (RTE). The rationale behind this choice is that the RTE is efficient in mitigating the degradation caused by the presence of impulsive noises while inducing little loss when the noise is Gaussian. Based on asymptotic results brought by recent tools from random matrix theory, we propose a design for the regularization parameter that maximizes the asymptotic detection probability under constant asymptotic false alarm rates. Provided Simulations support the efficiency of the proposed method, illustrating its gain over conventional settings of the regularization parameter.
dc.description.sponsorshipCouillet’s work is supported by the ANR Project RMT4GRAPH (ANR-14-CE28-0006).
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/8082486/
dc.rights(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectRegularized Tylers estimator
dc.subjectAdaptive Normalized Mached Filter
dc.subjectrobust detection
dc.subjectRandom Matrix Theory
dc.subjectOptimal design
dc.titleOptimal Design of the Adaptive Normalized Matched Filter Detector using Regularized Tyler Estimators
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.identifier.journalIEEE Transactions on Aerospace and Electronic Systems
dc.eprint.versionPost-print
dc.contributor.institutionLaboratoire des Signaux et Systémes (L2S, UMR CNRS 8506) CentraleSupélec-CNRS-Université Paris-Sud, 91192 Gif-sur-Yvette, France
kaust.personKammoun, Abla
kaust.personAlouini, Mohamed-Slim
refterms.dateFOA2018-06-13T10:44:50Z
dc.date.published-online2017-10-25
dc.date.published-print2018-04


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