Motion Parameter Estimation in the SAR System With Low PRF Sampling
Motion Parameter Estimation in the SAR System With Low PRF Sampling
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DOI：
10.1109/lgrs.2009.2039113
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发表时间：
2010-02
影响因子：
4.8
通讯作者：
Qisong Wu;M. Xing;Cheng-Wei Qiu;Baochang Liu;Z. Bao;T. Yeo
中科院分区：
文献类型：
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作者：
Qisong Wu;M. Xing;Cheng-Wei Qiu;Baochang Liu;Z. Bao;T. Yeo
提出了一种基于压缩感知（CS）理论的低脉冲重复频率（PRF）采样运动参数估计新方法。当脉冲重复频率小于多普勒频谱带宽时，运动目标同时存在多普勒中心频率模糊和多普勒频谱模糊。在这种情况下，传统的多普勒频域参数估计方法就失去了作用。本文的关键是将低重频采样合成孔径雷达系统中的运动参数估计问题转化为求解一个基于CS理论的优化方程。由于场景中的运动目标经过杂波对消后可以看作是稀疏信号，因此提出了一种基于CS理论的优化算法来重构稀疏信号，同时估计运动目标的沿航迹速度和方位角位置。考虑到运动目标的距离徙动不受重频的限制，采用Radon变换获得了目标的跨航迹速度和距离位置。仿真结果和真实的数据表明了该方法的有效性。
A novel approach to motion parameter estimation with low pulse repetition frequency (PRF) sampling based on compressed sensing (CS) theory is introduced. As is known to us, when PRF is less than the Doppler spectrum bandwidth, moving targets suffer both Doppler centroid frequency ambiguity and Doppler spectrum ambiguity. Under this condition, the traditional parameter estimation method in the Doppler domain is out of action. The key of this letter converts motion parameter estimation in the synthetic aperture radar system with low PRF sampling into solving an optimization equation based on CS theory. Because moving targets in the scene can be regarded as sparse signals after clutter cancellation, an optimization algorithm based on CS theory is proposed to reconstruct sparse signals and meanwhile estimate the along-track velocities and azimuth positions of moving targets. Considering the fact that range cell migration of moving targets is not subject to PRF limitations, Radon transform is adopted to obtain unambiguous across-track velocities and range positions. Results on simulation and real data are provided to show the effectiveness of this method.