Robust adaptive subspace detection in impulsive noise

Handle URI:
http://hdl.handle.net/10754/622596
Title:
Robust adaptive subspace detection in impulsive noise
Authors:
Ben Atitallah, Ismail ( 0000-0002-1748-1934 ) ; Kammoun, Abla ( 0000-0002-0195-3159 ) ; Alouini, Mohamed-Slim ( 0000-0003-4827-1793 ) ; Al-Naffouri, Tareq Y. ( 0000-0003-2843-5084 )
Abstract:
This paper addresses the design of the Adaptive Subspace Matched Filter (ASMF) detector in the presence of compound Gaussian clutters and a mismatch in the steering vector. In particular, we consider the case wherein the ASMF uses the regularized Tyler estimator (RTE) to estimate the clutter covariance matrix. Under this setting, a major question that needs to be addressed concerns the setting of the threshold and the regularization parameter. To answer this question, we consider the regime in which the number of observations used to estimate the RTE and their dimensions grow large together. Recent results from random matrix theory are then used in order to approximate the false alarm and detection probabilities by deterministic quantities. The latter are optimized in order to maximize an upper bound on the asymptotic detection probability while keeping the asymptotic false alarm probability at a fixed rate. © 2016 IEEE.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Electrical Engineering Program
Citation:
Ben Atitallah I, Kammoun A, Alouini M-S, Al-Naffouri TY (2016) Robust adaptive subspace detection in impulsive noise. 2016 IEEE Statistical Signal Processing Workshop (SSP). Available: http://dx.doi.org/10.1109/SSP.2016.7551750.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2016 IEEE Statistical Signal Processing Workshop (SSP)
Conference/Event name:
19th IEEE Statistical Signal Processing Workshop, SSP 2016
Issue Date:
13-Sep-2016
DOI:
10.1109/SSP.2016.7551750
Type:
Conference Paper
Additional Links:
http://ieeexplore.ieee.org/document/7551750/
Appears in Collections:
Conference Papers; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorBen Atitallah, Ismailen
dc.contributor.authorKammoun, Ablaen
dc.contributor.authorAlouini, Mohamed-Slimen
dc.contributor.authorAl-Naffouri, Tareq Y.en
dc.date.accessioned2017-01-02T09:55:32Z-
dc.date.available2017-01-02T09:55:32Z-
dc.date.issued2016-09-13en
dc.identifier.citationBen Atitallah I, Kammoun A, Alouini M-S, Al-Naffouri TY (2016) Robust adaptive subspace detection in impulsive noise. 2016 IEEE Statistical Signal Processing Workshop (SSP). Available: http://dx.doi.org/10.1109/SSP.2016.7551750.en
dc.identifier.doi10.1109/SSP.2016.7551750en
dc.identifier.urihttp://hdl.handle.net/10754/622596-
dc.description.abstractThis paper addresses the design of the Adaptive Subspace Matched Filter (ASMF) detector in the presence of compound Gaussian clutters and a mismatch in the steering vector. In particular, we consider the case wherein the ASMF uses the regularized Tyler estimator (RTE) to estimate the clutter covariance matrix. Under this setting, a major question that needs to be addressed concerns the setting of the threshold and the regularization parameter. To answer this question, we consider the regime in which the number of observations used to estimate the RTE and their dimensions grow large together. Recent results from random matrix theory are then used in order to approximate the false alarm and detection probabilities by deterministic quantities. The latter are optimized in order to maximize an upper bound on the asymptotic detection probability while keeping the asymptotic false alarm probability at a fixed rate. © 2016 IEEE.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/document/7551750/en
dc.subjectRandom matrix theoryen
dc.subjectRobust estimationen
dc.subjectSubspace detectionen
dc.titleRobust adaptive subspace detection in impulsive noiseen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentElectrical Engineering Programen
dc.identifier.journal2016 IEEE Statistical Signal Processing Workshop (SSP)en
dc.conference.date2016-06-25 to 2016-06-29en
dc.conference.name19th IEEE Statistical Signal Processing Workshop, SSP 2016en
dc.conference.locationPalma de Mallorca, ESPen
kaust.authorBen Atitallah, Ismailen
kaust.authorKammoun, Ablaen
kaust.authorAlouini, Mohamed-Slimen
kaust.authorAl-Naffouri, Tareq Y.en
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