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Remote Sens. 2017, 9(11), 1087; https://doi.org/10.3390/rs9111087

Non-Cooperative Bistatic SAR Clock Drift Compensation for Tomographic Acquisitions

1
Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milano, Italy
2
Comisión Nacional de Actividades Espaciales, 1063 Buenos Aires, Argentina
*
Author to whom correspondence should be addressed.
Received: 29 June 2017 / Revised: 14 September 2017 / Accepted: 19 October 2017 / Published: 25 October 2017
(This article belongs to the Special Issue Recent Advances in Polarimetric SAR Interferometry)
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Abstract

In the last years, an important amount of research has been headed towards the measurement of above-ground forest biomass with polarimetric Synthetic Aperture Radar (SAR) tomography techniques. This has motivated the proposal of future bistatic SAR missions, like the recent non-cooperative SAOCOM-CS and PARSIFAL from CONAE and ESA. It is well known that the quality of SAR tomography is directly related to the phase accuracy of the interferometer that, in the case of non-cooperative systems, can be particularly affected by the relative drift between onboard clocks. In this letter, we provide insight on the impact of the clock drift error on bistatic interferometry, as well as propose a correction algorithm to compensate its effect. The accuracy of the compensation is tested on simulated acquisitions over volumetric targets, estimating the final impact on tomographic profiles. View Full-Text
Keywords: bistatic SAR; clock drift; multisquint; PARSIFAL; phase calibration; SAR tomography; SAOCOM-CS; STALO bistatic SAR; clock drift; multisquint; PARSIFAL; phase calibration; SAR tomography; SAOCOM-CS; STALO
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Azcueta, M.; Tebaldini, S. Non-Cooperative Bistatic SAR Clock Drift Compensation for Tomographic Acquisitions. Remote Sens. 2017, 9, 1087.

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