Noise-Robust ISAR Translational Motion Compensation via HLPT-GSCFT
| Autor: | Fengkai Liu, Darong Huang, Xinrong Guo, Cunqian Feng |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Remote Sensing, Vol 14, Iss 24, p 6201 (2022) |
| Druh dokumentu: | article |
| ISSN: | 14246201 2072-4292 |
| DOI: | 10.3390/rs14246201 |
| Popis: | Translational motion compensation is a prerequisite of inverse synthetic aperture radar (ISAR) imaging. Translational motion compensation for datasets with low signal-to-noise ratio (SNR) is important but challenging. In this work, we proposed a noise-robust translational motion compensation method based on high-order local polynomial transform–generalized scaled Fourier transform (HLPT-GSCFT). We first model the translational motion as a fourth-order polynomial according to order-of-magnitude analysis, and then design HLPT-GSCFT for translation parameter estimation and parametric translational motion compensation. Specifically, HLPT is designed to estimate the acceleration and third-order acceleration of the translational motion and GSCFT is introduced to estimate the second-order acceleration. Both HLPT and GSCFT have a strong ability for cross-term suppression. In addition, we use a minimum weighted entropy algorithm to estimate the velocity of the translational motion, which can improve the noise robustness of the parameter estimation. Experimental results based on a measured dataset prove that the proposed method is effective and noise-robust. |
| Databáze: | Directory of Open Access Journals |
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