Obtained by wide band radar system, high resolution range profile (HRRP) is the projection of scatterers of target to the radar line-of-sight (LOS). HRRP reconstruction is unavoidable for inverse synthetic aperture radar (ISAR) imaging, and of particular usage for target recognition, especially in cases that the ISAR image of target is not able to be achieved. For the high-speed moving target, however, its HRRP is stretched by the high order phase error. To obtain well-focused HRRP, the phase error induced by target velocity should be compensated, utilizing either measured or estimated target velocity. Noting in case of under-sampled data, the traditional velocity estimation and HRRP reconstruction algorithms become invalid, a novel HRRP reconstruction of high-speed target for under-sampled data is proposed. The Laplacian scale mixture (LSM) is used as the sparse prior of HRRP, and the variational Bayesian inference is utilized to derive its posterior, so as to reconstruct it with high resolution from the under-sampled data. Additionally, during the reconstruction of HRRP, the target velocity is estimated via joint constraint of entropy minimization and sparseness of HRRP to compensate the high order phase error brought by the target velocity to concentrate HRRP. Experimental results based on both simulated and measured data validate the effectiveness of the proposed Bayesian HRRP reconstruction algorithm.

中文翻译：

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欠采样数据高速移动目标的贝叶斯高分辨率距离剖面重建


高分辨率距离剖面（HRRP）是由宽带雷达系统获得的，是目标散射体在雷达视距（LOS）上的投影。 HRRP重建对于逆合成孔径雷达(ISAR)成像是不可避免的，特别是在目标识别中，特别是在无法获得目标ISAR图像的情况下。然而，对于高速运动目标，其 HRRP 会因高阶相位误差而被拉伸。为了获得聚焦良好的 HRRP，应利用测量的或估计的目标速度来补偿由目标速度引起的相位误差。针对欠采样数据时传统的速度估计和HRRP重建算法失效的问题，提出了一种针对欠采样数据的高速目标HRRP重建算法。采用拉普拉斯尺度混合（LSM）作为HRRP的稀疏先验，并利用变分贝叶斯推理推导其后验，从而从欠采样数据中高分辨率地重建它。另外，在HRRP重构过程中，通过熵最小化和HRRP稀疏性的联合约束来估计目标速度，以补偿目标速度带来的高阶相位误差以集中HRRP。基于模拟和测量数据的实验结果验证了所提出的贝叶斯 HRRP 重建算法的有效性。