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Stabilized Detection Accuracy Maximization using Adaptive SAR Image Processing in LEO Networks

Authors
Kim, K.Lee, J.Jung, S.Kim, J.Kim, J.
Issue Date
2022
Publisher
Institute of Electrical and Electronics Engineers Inc.
Keywords
adaptive filtering; Adaptive filters; Filtering algorithms; Filtering theory; Low Earth orbit; Lyapunov optimization; Marine vehicles; Radar polarimetry; Satellites; Stability analysis; synthetic aperture radar; target detection
Citation
IEEE Transactions on Vehicular Technology
Indexed
SCIE
SCOPUS
Journal Title
IEEE Transactions on Vehicular Technology
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/142099
DOI
10.1109/TVT.2022.3154604
ISSN
0018-9545
Abstract
The use of low Earth orbit (LEO) satellites for world-wide surveillance services is currently actively discussed and developed because the constellation of satellites is one major approach which can provide global seamless network services. Because synthetic aperture radar (SAR), which is used for satellite image acquisition and its related signal processing, is dealing with large volumes of image data, corresponding on-demand adaptive methods for SAR image processing are essentially required for stabilized surveillance services under the consideration of data burst situations. Thus, an adaptive vision algorithm for ship detection which is one of major tasks in SAR image processing researches is proposed based on Lyapunov optimization framework, which maximizes the detection performance while satisfying stability conditions. The high-performance filters are utilized for precisely recognizing the targets whereas they introduce relatively larger delays (i.e., tradeoff exists between performances and delays). Therefore, the proposed Lyapunov optimization-based adaptive filter selection algorithm is designed based on the characteristics. Our data-intensive performance evaluation results prove that the proposed algorithm achieves desired performance improvements. IEEE
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Kim, Joong heon
공과대학 (전기전자공학부)
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