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首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >High-Resolution ISAR Imaging With Sparse Stepped-Frequency Waveforms
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High-Resolution ISAR Imaging With Sparse Stepped-Frequency Waveforms

机译：具有稀疏步进频率波形的高分辨率ISAR成像

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摘要

From the theory of compressive sensing (CS), we know that the exact recovery of an unknown sparse signal can be achieved from limited measurements by solving a sparsity-constrained optimization problem. For inverse synthetic aperture radar (ISAR) imaging, the backscattering field of a target is usually composed of contributions by a very limited amount of strong scattering centers, the number of which is much smaller than that of pixels in the image plane. In this paper, a novel framework for ISAR imaging is proposed through sparse stepped-frequency waveforms (SSFWs). By using the framework, the measurements, only at some portions of frequency subbands, are used to reconstruct full-resolution images by exploiting sparsity. This waveform strategy greatly reduces the amount of data and acquisition time and improves the antijamming capability. A new algorithm, named the sparsity-driven High-Resolution Range Profile (HRRP) synthesizer, is presented in this paper to overcome the error phase due to motion usually degrading the HHRP synthesis. The sparsity-driven HRRP synthesizer is robust to noise. The main novelty of the proposed ISAR imaging framework is twofold: 1) dividing the motion compensation into three steps and therefore allowing for very accurate estimation and 2) both sparsity and signal-to-noise ratio are enhanced dramatically by coherent integrant in cross-range before performing HRRP synthesis. Both simulated and real measured data are used to test the robustness of the ISAR imaging framework with SSFWs. Experimental results show that the framework is capable of precise reconstruction of ISAR images and effective suppression of both phase error and noise.
机译：从压缩感测（CS）的理论，我们知道可以通过解决稀疏约束优化问题，从有限的测量中获得未知稀疏信号的精确恢复。对于逆合成孔径雷达（ISAR）成像，目标的反向散射场通常由非常有限数量的强散射中心所贡献，这些散射中心的数量远小于像平面中像素的数量。本文通过稀疏的步进频率波形（SSFW）提出了一种新颖的ISAR成像框架。通过使用该框架，仅在频率子带的某些部分进行测量，以利用稀疏性来重建全分辨率图像。这种波形策略大大减少了数据量和采集时间，并提高了抗干扰能力。本文提出了一种新的算法，称为稀疏驱动的高分辨率距离剖面（HRRP）合成器，以克服由于运动而引起的错误相位，通常会降低HHRP的合成效率。稀疏驱动的HRRP合成器对噪声具有鲁棒性。所提出的ISAR成像框架的主要新颖之处在于两个方面：1）将运动补偿分为三个步骤，因此可以进行非常精确的估计； 2）跨范围内的相干积分可显着提高稀疏性和信噪比在执行HRRP综合之前。模拟和实际测量数据均用于测试带有SSFW的ISAR成像框架的鲁棒性。实验结果表明，该框架能够精确重构ISAR图像，并有效抑制相位误差和噪声。

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