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Motion Estimation and Improved SAR Imaging for Agile Platforms Using Omnidirectional Radar and INS Sensor Fusion

Accurate motion estimation is a challenging problem for agile radar platforms, even when state-of-the-art inertial navigation sensors (INSs) are used. However, it is an important problem to solve as it can have a large impact on the performance of radar modes, such as synthetic aperture radar (SAR)....

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 2023-02, Vol.59 (1), p.153-171
Main Authors: Klein, Keith T. J., Uysal, Faruk, Cuenca, Miguel Caro, Otten, Matern P. G., de Wit, Jacco J. M.
Format: Article
Language:English
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Summary:Accurate motion estimation is a challenging problem for agile radar platforms, even when state-of-the-art inertial navigation sensors (INSs) are used. However, it is an important problem to solve as it can have a large impact on the performance of radar modes, such as synthetic aperture radar (SAR). This study addresses the motion estimation problem of agile radar platforms from the perspective of an omnidirectional radar array. In this study, we perform an analysis on the applicability of an omnidirectional radar array to explicitly estimate the motion of an agile SAR platform and improve imaging quality. Building on existing 1-D SAR motion compensation techniques, we develop a method to estimate the 3-D motion of the radar platform utilizing its height and velocity vector. Using a prototype radar developed at the Netherlands Organisation for Applied Scientific Research, we experimentally verify that using the proposed velocity estimation method alone, we achieve comparable positioning performance to that of a state-of-the-art INS, making it possible to perform INS-free SAR imaging by using arbitrary flight paths. We also show that fusing the radar positioning estimates obtained from the proposed methods with the INS output yields an additional increase in SAR imaging performance, improving the resolvability and detectability of weak targets.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2022.3190459