Optimal Design of Large Dimensional Adaptive Subspace Detectors

Handle URI:
http://hdl.handle.net/10754/611333
Title:
Optimal Design of Large Dimensional Adaptive Subspace Detectors
Authors:
Ben Atitallah, Ismail ( 0000-0002-1748-1934 ) ; Kammoun, Abla ( 0000-0002-0195-3159 ) ; Alouini, Mohamed-Slim ( 0000-0003-4827-1793 ) ; Alnaffouri, Tareq Y.
Abstract:
This paper addresses the design of Adaptive Subspace Matched Filter (ASMF) detectors in the presence of a mismatch in the steering vector. These detectors are coined as adaptive in reference to the step of utilizing an estimate of the clutter covariance matrix using training data of signalfree observations. To estimate the clutter covariance matrix, we employ regularized covariance estimators that, by construction, force the eigenvalues of the covariance estimates to be greater than a positive scalar . While this feature is likely to increase the bias of the covariance estimate, it presents the advantage of improving its conditioning, thus making the regularization suitable for handling high dimensional regimes. In this paper, we consider the setting of the regularization parameter and the threshold for ASMF detectors in both Gaussian and Compound Gaussian clutters. In order to allow for a proper selection of these parameters, it is essential to analyze the false alarm and detection probabilities. For tractability, such a task is carried out under the asymptotic regime in which the number of observations and their dimensions grow simultaneously large, thereby allowing us to leverage existing results from random matrix theory. Simulation results are provided in order to illustrate the relevance of the proposed design strategy and to compare the performances of the proposed ASMF detectors versus Adaptive normalized Matched Filter (ANMF) detectors under mismatch scenarios.
KAUST Department:
Electrical Engineering Program
Citation:
Optimal Design of Large Dimensional Adaptive Subspace Detectors 2016:1 IEEE Transactions on Signal Processing
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Signal Processing
Issue Date:
27-May-2016
DOI:
10.1109/TSP.2016.2573750
Type:
Article
ISSN:
1053-587X; 1941-0476
Sponsors:
This work was funded in part by a CRG2 grant CRG R2 13 ALOU KAUST 2 from the Office of Competitive Research (OCRF) at King Abdullah University of Science and Technology (KAUST).
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7480418
Appears in Collections:
Articles; Electrical Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorBen Atitallah, Ismailen
dc.contributor.authorKammoun, Ablaen
dc.contributor.authorAlouini, Mohamed-Slimen
dc.contributor.authorAlnaffouri, Tareq Y.en
dc.date.accessioned2016-06-01T06:47:54Z-
dc.date.available2016-06-01T06:47:54Z-
dc.date.issued2016-05-27-
dc.identifier.citationOptimal Design of Large Dimensional Adaptive Subspace Detectors 2016:1 IEEE Transactions on Signal Processingen
dc.identifier.issn1053-587X-
dc.identifier.issn1941-0476-
dc.identifier.doi10.1109/TSP.2016.2573750-
dc.identifier.urihttp://hdl.handle.net/10754/611333-
dc.description.abstractThis paper addresses the design of Adaptive Subspace Matched Filter (ASMF) detectors in the presence of a mismatch in the steering vector. These detectors are coined as adaptive in reference to the step of utilizing an estimate of the clutter covariance matrix using training data of signalfree observations. To estimate the clutter covariance matrix, we employ regularized covariance estimators that, by construction, force the eigenvalues of the covariance estimates to be greater than a positive scalar . While this feature is likely to increase the bias of the covariance estimate, it presents the advantage of improving its conditioning, thus making the regularization suitable for handling high dimensional regimes. In this paper, we consider the setting of the regularization parameter and the threshold for ASMF detectors in both Gaussian and Compound Gaussian clutters. In order to allow for a proper selection of these parameters, it is essential to analyze the false alarm and detection probabilities. For tractability, such a task is carried out under the asymptotic regime in which the number of observations and their dimensions grow simultaneously large, thereby allowing us to leverage existing results from random matrix theory. Simulation results are provided in order to illustrate the relevance of the proposed design strategy and to compare the performances of the proposed ASMF detectors versus Adaptive normalized Matched Filter (ANMF) detectors under mismatch scenarios.en
dc.description.sponsorshipThis work was funded in part by a CRG2 grant CRG R2 13 ALOU KAUST 2 from the Office of Competitive Research (OCRF) at King Abdullah University of Science and Technology (KAUST).en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7480418en
dc.rights(c) 2016 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.en
dc.subjectAdaptive Normalized Matched Filtersen
dc.subjectAdaptive Subspace Matched Filtersen
dc.subjectcovariance matrix estimationen
dc.titleOptimal Design of Large Dimensional Adaptive Subspace Detectorsen
dc.typeArticleen
dc.contributor.departmentElectrical Engineering Programen
dc.identifier.journalIEEE Transactions on Signal Processingen
dc.eprint.versionPost-printen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorBen Atitallah, Ismailen
kaust.authorKammoun, Ablaen
kaust.authorAlouini, Mohamed-Slimen
kaust.authorY. Alnaffouri, Tareqen
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