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| Adaptive Measurement Matrix Optimization for ISAR Imaging with Sparse Frequency-stepped Chirp Signals |
| CHEN Yijun① LI Kaiming① ZHANG Qun①②③ LUO Ying①②③ |
①(Institute of Information and Navigation, Air Force Engineering University, Xi’an 710077, China)
②(Collaborative Innovation Center of Information Sensing and Understanding, Xi’an 710077, China)
③(Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), Fudan University, Shanghai 200433, China) |
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Abstract The ISAR imaging technology with sparse Stepped-Frequency Chirp Signals (SFCS) based on Compressive Sensing (CS) theory can construct the target image from a few of measurements with high probability, where the measurement matrix optimization is an effective way of improving the imaging quality and reducing the measurements. However, most of the existing measurement matrix optimization methods do not utilize the target characteristic, which leads to low adaptive ability of target. Therefore, an adaptive measurement matrix optimization method for Inverse Synthetic Aperture Radar (ISAR) Imaging with sparse SFCS is proposed in this paper, where the actual physical observation process is considered and the target characteristics are utilized to optimize the measurement matrix. In the method, a parametric sparse representation model of ISAR imaging is established to solve the Doppler sensitivity firstly. On the basis, the measurement matrix is optimized with the goal of obtaining the best target image with the minimum measurements under a given image quality requirement. As a result, the expected imaging results can be obtained with minimum measurements by using the optimized measurement matrix. The effectiveness of the proposed method is demonstrated by experiments.
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Received: 08 June 2017
Published: 12 December 2017
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| Fund:The National Natural Science Foundation of China (61631019, 61471386), The Youth Science and Technology New Star Program of Shaanxi Province (2016KJXX-49) |
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Corresponding Authors:
CHEN Yijun
E-mail: chenyijun519@126.com
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