Zhang Shuanghui, Liu Yongxiang, Li Xiang, Bi Guoan
IEEE Trans Image Process. 2020 Mar 18. doi: 10.1109/TIP.2020.2980149.
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.
高分辨率距离像（HRRP）是通过宽带雷达系统获取的目标散射点在雷达视线（LOS）方向上的投影。HRRP重建是逆合成孔径雷达（ISAR）成像中不可避免的环节，在目标识别中具有特殊用途，特别是在无法获取目标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拉伸。为了获得聚焦良好的HRRP，应利用测量或估计的目标速度来补偿目标速度引起的相位误差。注意到在欠采样数据情况下，传统的速度估计和HRRP重建算法会失效，本文提出了一种针对欠采样数据的高速目标HRRP重建新方法。将拉普拉斯尺度混合（LSM）用作HRRP的稀疏先验，利用变分贝叶斯推理推导其后验分布，从而从欠采样数据中高分辨率地重建HRRP。此外，在HRRP重建过程中，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，使HRRP聚焦。基于模拟数据和实测数据的实验结果验证了所提贝叶斯HRRP重建算法的有效性。
