Filip-Dhaubhadel, Alexandra und Shutin, Dmitriy (2020) Long Coherent Integration in Passive Radar Systems Using Super-Resolution Sparse Bayesian Learning. IEEE Transactions on Aerospace and Electronic Systems, 57 (1), Seiten 554-572. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TAES.2020.3026844. ISSN 0018-9251.
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Offizielle URL: https://ieeexplore.ieee.org/document/9206073
Kurzfassung
Maximizing the coherent processing interval (CPI) is crucial when performing passive radar detection on weak signal reflections. In practice however, the CPI is limited by the target movement. In this work, the extent of the range and Doppler migration effects occurring when using a long CPI to integrate the returns from an L-band digital aeronautical communication system (LDACS) based passive radar is studied. In particular, our simulations underline the extensive Doppler migration effect that arises even for non-accelerating targets. To this end, the Keystone transform and fractional Fourier transform techniques are combined with the standard passive radar processing to enable the compensation of both range and Doppler migration effects. This non-model based approach is, however, shown to have limitations, in particular for low signal-to-noise ratios and/or multi-target scenarios. To address these shortcomings, a novel model-based framework that allows to perform joint target detection and parameter estimation is developed. For this, a superresolution sparse Bayesian learning approach is employed. This technique uses a multi-target observation model which accurately accounts for the underlying range and Doppler migration effects and provides super-resolution estimation capabilities. This is particularly advantageous in the LDACS case since the narrow bandwidth generally limits the separation of closely spaced targets. The simulation experiments demonstrate the effectiveness of the algorithm and the advantages it provides when compared to the standard migration compensation approach.
elib-URL des Eintrags: | https://elib.dlr.de/136155/ | ||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||
Zusätzliche Informationen: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works | ||||||||||||
Titel: | Long Coherent Integration in Passive Radar Systems Using Super-Resolution Sparse Bayesian Learning | ||||||||||||
Autoren: |
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Datum: | 2020 | ||||||||||||
Erschienen in: | IEEE Transactions on Aerospace and Electronic Systems | ||||||||||||
Referierte Publikation: | Ja | ||||||||||||
Open Access: | Ja | ||||||||||||
Gold Open Access: | Nein | ||||||||||||
In SCOPUS: | Ja | ||||||||||||
In ISI Web of Science: | Ja | ||||||||||||
Band: | 57 | ||||||||||||
DOI: | 10.1109/TAES.2020.3026844 | ||||||||||||
Seitenbereich: | Seiten 554-572 | ||||||||||||
Verlag: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||
ISSN: | 0018-9251 | ||||||||||||
Status: | veröffentlicht | ||||||||||||
Stichwörter: | coherent processing, range and Doppler migration, LDACS, passive radar, keystone transform, fractional Fourier transform, FrFT, sparse Bayesian learning, super-resolution | ||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||
HGF - Programm: | Luftfahrt | ||||||||||||
HGF - Programmthema: | Luftverkehrsmanagement und Flugbetrieb | ||||||||||||
DLR - Schwerpunkt: | Luftfahrt | ||||||||||||
DLR - Forschungsgebiet: | L AO - Air Traffic Management and Operation | ||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | L - Kommunikation, Navigation und Überwachung (alt) | ||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||
Institute & Einrichtungen: | Institut für Kommunikation und Navigation > Nachrichtensysteme | ||||||||||||
Hinterlegt von: | Filip-Dhaubhadel, Dr. Alexandra | ||||||||||||
Hinterlegt am: | 01 Okt 2020 16:20 | ||||||||||||
Letzte Änderung: | 24 Okt 2023 12:54 |
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