Proceedings of the KSRS Conference (대한원격탐사학회:학술대회논문집)
- 2008.03a
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- Pages.158-163
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- 2008
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- 1226-9743(pISSN)
Application of Subarray Averaging and Entropy Minimization Algorithm to Stepped-Frequency ISAR Autofocus
부배열 평균과 엔트로피 최소화 기법을 이용한 stepped-frequency ISAR 자동초점 기법 성능 향상 연구
- Jeong, Ho-Ryung ;
- Kim, Kyung-Tae ;
- Lee, Dong-Han ;
- Seo, Du-Chun ;
- Song, Jeong-Heon ;
- Choi, Myung-Jin ;
- Lim, Hyo-Suk
- 정호령 (한국항공우주연구원) ;
- 김경태 (영남대학교) ;
- 이동한 (한국항공우주연구원) ;
- 서두천 (한국항공우주연구원) ;
- 송정헌 (한국항공우주연구원) ;
- 최명진 (한국항공우주연구원) ;
- 임효숙 (한국항공우주연구원)
- Published : 2008.03.21
Abstract
In inverse synthetic aperture radar (ISAR) imaging, An ISAR autofocusing algorithm is essential to obtain well-focused ISAR images. Traditional methods have relied on the approximation that the phase error due to target motion is a function of the cross-range dimension only. However, in the stepped-frequency radar system, it tends to become a two-dimensional function of both down-range and cross-range, especially when target's movement is very fast and the pulse repetition frequency (PRF) is low. In order to remove the phase error along down-range, this paper proposes a method called SAEM (subarray averaging and entropy minimization) [1] that uses a subarray averaging concept in conjunction with the entropy cost function in order to find target motion parameters, and a novel 2-D optimization technique with the inherent properties of the proposed entropy-based cost function. A well-focused ISAR image can be obtained from the combination of the proposed method and a traditional autofocus algorithm that removes the phase error along the cross-range dimension. The effectiveness of this method is illustrated and analyzed with simulated targets comprised of point scatters.
Keywords