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Estimation of Overspread Scattering Functions

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Pfander, Götz E. ; Zheltov, P.:
Estimation of Overspread Scattering Functions.
In: IEEE Transactions on Signal Processing. 63 (Mai 2015) 10. - S. 2451-2463.
ISSN 1053-587x ; 1941-0476

Volltext

Volltext Link zum Volltext (externe URL):
http://ieeexplore.ieee.org/document/7041229/

Kurzfassung/Abstract

In many radar scenarios, the radar target or the medium is assumed to possess randomly varying parts. The properties of the target are described by a random process known as the spreading function. Its second order statistics under the WSSUS assumption are given by the scattering function. Recent developments in operator sampling theory suggest novel channel sounding procedures that allow for the determination of the spreading function given complete statistical knowledge of the operator echo from a single sounding by a weighted pulse train. We construct and analyze a novel estimator for the scattering function based on these findings. Our results apply whenever the scattering function is supported on a compact subset of the time-frequency plane. We do not make any restrictions either on the geometry of this support set, or on its area. Our estimator can be seen as a generalization of the averaged periodogram estimator for the case of a non-rectangular geometry of the support set of the scattering function.

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Publikationsform:Artikel
Schlagwörter:electromagnetic wave scattering; radar target recognition; WSSUS assumption; channel sounding procedures; overspread scattering function estimation; periodogram estimator; radar scenarios; radar target; random process; scattering function; second order statistics; spreading function; statistical knowledge; time-frequency plane; weighted pulse train; Channel estimation; Estimation; Geometry; Radar; Scattering; Stochastic processes; Time-frequency analysis; Fiducial vectors; finite dimensional Gabor systems; sampling of operators; scattering function; spreading function
Institutionen der Universität:Mathematisch-Geographische Fakultät > Mathematik > Lehrstuhl für Mathematik - Wissenschaftliches Rechnen
Mathematisch-Geographische Fakultät > Mathematik > Mathematisches Institut für Maschinelles Lernen und Data Science (MIDS)
DOI / URN / ID:10.1109/TSP.2015.2403309
Open Access: Freie Zugänglichkeit des Volltexts?:Nein
Peer-Review-Journal:Ja
Verlag:IEEE
Die Zeitschrift ist nachgewiesen in:
Titel an der KU entstanden:Ja
KU.edoc-ID:20369
Eingestellt am: 16. Aug 2017 09:17
Letzte Änderung: 04. Okt 2024 13:22
URL zu dieser Anzeige: https://edoc.ku.de/id/eprint/20369/
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