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Long Coherent Integration in Passive Radar Systems Using Super-Resolution Sparse Bayesian Learning

Filip-Dhaubhadel, Alexandra and 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), pp. 554-572. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TAES.2020.3026844. ISSN 0018-9251.

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Official URL: https://ieeexplore.ieee.org/document/9206073

Abstract

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.

Item URL in elib:https://elib.dlr.de/136155/
Document Type:Article
Additional Information:© 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
Title:Long Coherent Integration in Passive Radar Systems Using Super-Resolution Sparse Bayesian Learning
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Filip-Dhaubhadel, AlexandraUNSPECIFIEDhttps://orcid.org/0000-0002-7426-1081UNSPECIFIED
Shutin, DmitriyUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:2020
Journal or Publication Title:IEEE Transactions on Aerospace and Electronic Systems
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:57
DOI:10.1109/TAES.2020.3026844
Page Range:pp. 554-572
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0018-9251
Status:Published
Keywords:coherent processing, range and Doppler migration, LDACS, passive radar, keystone transform, fractional Fourier transform, FrFT, sparse Bayesian learning, super-resolution
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:air traffic management and operations
DLR - Research area:Aeronautics
DLR - Program:L AO - Air Traffic Management and Operation
DLR - Research theme (Project):L - Communication, Navigation and Surveillance (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Communication and Navigation > Communications Systems
Deposited By: Filip-Dhaubhadel, Dr. Alexandra
Deposited On:01 Oct 2020 16:20
Last Modified:24 Oct 2023 12:54

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