稀疏孔径ISAR机动目标成像与相位补偿方法
收稿日期: 2013-10-12
修回日期: 2014-04-08
网络出版日期: 2014-04-16
基金资助
国家自然科学基金（61301280）；中央高校基本科研业务费专项资金（K5051302001，K5051302038）
ISAR Phase Compensation and Imaging of Maneuvering Target with Sparse Apertures
Received date: 2013-10-12
Revised date: 2014-04-08
Online published: 2014-04-16
Supported by
National Natural Science Foundation of China (61301280); the Fundamental Research Funds for the Central Universities (K5051302001, K5051302038)
现代逆合成孔径雷达（ISAR）可实现多模式下对多目标的交替测量工作，但波束切换使得任一目标的方位孔径具有稀疏性，同时目标的机动性也使得目标的方位维信号具有高次调制特性，这均导致以连续采样信号模型为基础的传统ISAR运动补偿和成像方法难以直接应用。本文提出一种新的稀疏孔径（SA）ISAR机动目标相位补偿和成像方法。该方法首先建立了稀疏孔径ISAR机动目标的信号模型，然后通过求解2范数最大化的优化函数，实现对相位误差的精确估计，并且通过迭代处理提高了相位误差的估计精度。相位误差补偿后，根据压缩感知理论，构造了机动目标情况下的Chirp-Fourier字典，通过对稀疏优化问题的求解，能够较精确地从稀疏孔径信号中重构出机动目标的全孔径（FA）信号。仿真和实测数据处理结果证明了本文方法的有效性。
黄大荣 , 郭新荣 , 张磊 , 邢孟道 , 保铮 . 稀疏孔径ISAR机动目标成像与相位补偿方法[J]. 航空学报, 2014 , 35(7) : 2019 -2030 . DOI: 10.7527/S1000-6893.2013.0043
The modern inverse synthetic aperture radar (ISAR) has the function of surveying multiple targets in different operating modes. But the apertures of any target are sparse because of the beam switching. The targets always navigate using a maneuvering way at the same time, which lead to the cross-range signals modeled as high-order frequency signals. Both the traditional ISAR phase compensation method and imaging method based on the continuous sampling signal models are inadequate in the maneuvering case. New phase compensation and imaging method for the maneuvering target with sparse aperture (SA) is proposed in this paper. Firstly, the signal model of the sparse apeture ISAR for maneuvering targets is founded, then the phase error terms are estimated precisely by solving the maximum norm-2 optimization, and the estimated accuracy of phase error terms is reinforced by embedding iterations. After phase error compensation, the redundant chirp-Fourier dictionary of the maneuvering target is built by the compressed sensing theory, and the full apertures (TA) data can be reconstructed from the SA measurements precisely by solving the sparsity-driven optimization. The results of simulated data and real measured data verify the validity of the proposed algorithm.
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