Adaptive Measurement Matrix Optimization for ISAR Imaging with Sparse Frequency-stepped Chirp Signals

CHEN Yijun; LI Kaiming; ZHANG Qun; LUO Ying

doi:10.11999/JEIT170554

Volume 40 Issue 3

Mar. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(3): 509-516

CHEN Yijun, LI Kaiming, ZHANG Qun, LUO Ying. Adaptive Measurement Matrix Optimization for ISAR Imaging with Sparse Frequency-stepped Chirp Signals[J]. Journal of Electronics & Information Technology, 2018, 40(3): 509-516. doi: 10.11999/JEIT170554

Citation:

CHEN Yijun, LI Kaiming, ZHANG Qun, LUO Ying. Adaptive Measurement Matrix Optimization for ISAR Imaging with Sparse Frequency-stepped Chirp Signals[J]. Journal of Electronics & Information Technology, 2018, 40(3): 509-516. doi: 10.11999/JEIT170554

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Adaptive Measurement Matrix Optimization for ISAR Imaging with Sparse Frequency-stepped Chirp Signals

doi: 10.11999/JEIT170554

1.
(Institute of Information and Navigation, Air Force Engineering University, Xi&rsquo
2.

Funds:

The National Natural Science Foundation of China (61631019, 61471386), The Youth Science and Technology New Star Program of Shaanxi Province (2016KJXX-49)

Received Date: 2017-06-08
Rev Recd Date: 2017-11-08
Publish Date: 2018-03-19

Abstract

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.
- Measurement matrix optimization,
- Parametric sparse representation,
- Sparse Stepped-Frequency Chirp Signals (SFCS)

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References(20)

References

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