Gollin, Nicola and Martone, Michele and Villano, Michelangelo and Rizzoli, Paola and Krieger, Gerhard (2019) Predictive Quantization for Staggered Synthetic Aperture Radar. In: 2019 12th German Microwave Conference (GeMiC), pp. 1-4. IEEE. German Microwave Conference (GeMiC), 2019-03-25 - 2019-03-27, Stuttgart, Germany. doi: 10.23919/GEMIC.2019.8698197. ISBN 978 3 9812668 9 4. ISSN 2167-8022.
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Official URL: https://ieeexplore.ieee.org/document/8698197
Abstract
For upcoming spaceborne SAR mission the amount of data collected onboard is increasing, due to the employment of large bandwidths, multiple polarizations, and large swath widths, which lead to hard requirements in terms of onboard memory and downlink capacity. In this context, SAR raw data quantization represents an essential aspect, since it affects both the amount of data to be stored and transmitted to the ground and the quality of the resulting SAR products. In this paper, a data reduction approach based on predictive quantization is investigated in the context of Tandem-L, a DLR proposal for a highly innovative bistatic L-band radar satellite mission, aimed at monitoring the dynamic processes of the Earth. The proposed technique takes advantage of the time-variant autocorrelation properties of the non-uniform azimuth raw data stream in order to reduce the amount of data through a novel quantization method, named Predictive-Block Adaptive Quantization. Different prediction orders are investigated by considering the trade-off between achievable performance and complexity. Simulations for different target scenarios show that a data reduction of about 17.5% can be achieved with the proposed technique with a modest increase of the system complexity. Moreover, having a priori information on the gap positions in staggered SAR, a technique for their reconstruction based on dynamic bit allocation has been successfully implemented as well, showing no significant loss of information.
Item URL in elib: | https://elib.dlr.de/126697/ | ||||||||||||||||||||||||
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Document Type: | Conference or Workshop Item (Speech) | ||||||||||||||||||||||||
Title: | Predictive Quantization for Staggered Synthetic Aperture Radar | ||||||||||||||||||||||||
Authors: |
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Date: | 2019 | ||||||||||||||||||||||||
Journal or Publication Title: | 2019 12th German Microwave Conference (GeMiC) | ||||||||||||||||||||||||
Refereed publication: | Yes | ||||||||||||||||||||||||
Open Access: | No | ||||||||||||||||||||||||
In SCOPUS: | No | ||||||||||||||||||||||||
In ISI Web of Science: | No | ||||||||||||||||||||||||
DOI: | 10.23919/GEMIC.2019.8698197 | ||||||||||||||||||||||||
Page Range: | pp. 1-4 | ||||||||||||||||||||||||
Publisher: | IEEE | ||||||||||||||||||||||||
ISSN: | 2167-8022 | ||||||||||||||||||||||||
ISBN: | 978 3 9812668 9 4 | ||||||||||||||||||||||||
Status: | Published | ||||||||||||||||||||||||
Keywords: | Synthetic Aperture Radar, Quantization, Predictive Coding, Staggered SAR, Tandem-L | ||||||||||||||||||||||||
Event Title: | German Microwave Conference (GeMiC) | ||||||||||||||||||||||||
Event Location: | Stuttgart, Germany | ||||||||||||||||||||||||
Event Type: | international Conference | ||||||||||||||||||||||||
Event Start Date: | 25 March 2019 | ||||||||||||||||||||||||
Event End Date: | 27 March 2019 | ||||||||||||||||||||||||
HGF - Research field: | Aeronautics, Space and Transport | ||||||||||||||||||||||||
HGF - Program: | Space | ||||||||||||||||||||||||
HGF - Program Themes: | Earth Observation | ||||||||||||||||||||||||
DLR - Research area: | Raumfahrt | ||||||||||||||||||||||||
DLR - Program: | R EO - Earth Observation | ||||||||||||||||||||||||
DLR - Research theme (Project): | R - Vorhaben Tandem-L Vorstudien (old) | ||||||||||||||||||||||||
Location: | Oberpfaffenhofen | ||||||||||||||||||||||||
Institutes and Institutions: | Microwaves and Radar Institute > Spaceborne SAR Systems Microwaves and Radar Institute > Radar Concepts | ||||||||||||||||||||||||
Deposited By: | Gollin, Nicola | ||||||||||||||||||||||||
Deposited On: | 04 Mar 2019 10:28 | ||||||||||||||||||||||||
Last Modified: | 24 Apr 2024 20:30 |
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