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Super-resolution ISAR imaging based on the modified iterative reweighted approach

机译:基于改进的迭代重新重量方法的超分辨率ISAR成像

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High-resolution inverse synthetic aperture radar (ISAR) imaging of fast rotating targets is a complicated task. Targets' fast rotational motion always introduces migration through range cells (MTRC) when we use the conventional range Doppler algorithm. We mainly focus on the ISAR imaging of a fast rotating target. In that case, the samples in the cross-range dimension are insufficient, which is the undersampling case. Compressed sensing-based ISAR imaging methods are generally faced with the problem of basis mismatch, which may degrade the ISAR image. A two-dimensional iterative reweighted super-resolution algorithm is proposed by iteratively decreasing a surrogate function. We also compared the performance of the proposed method with other state-of-the-art sparse recovery methods. Simulation results show that the proposed method can achieve the high-resolution ISAR imaging of fast rotating targets. Moreover, the MTRC and the basis mismatch problems can be effectively solved. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)
机译:快速旋转目标的高分辨率逆合成孔径雷达(ISAR)成像是一个复杂的任务。当我们使用传统范围多普勒算法时,目标快速旋转运动始终通过范围细胞(MTRC)介绍迁移。我们主要专注于快速旋转目标的ISAR成像。在这种情况下,交叉范围尺寸中的样品不足,这是欠采样情况。基于压缩的感测的ISAR成像方法通常面临基础失配的问题,这可能降低了ISAR图像。通过迭代地减少代理函数来提出一种二维迭代重新重量超分辨率算法。我们还将提出的方法与其他最先进的稀疏恢复方法进行了比较。仿真结果表明,该方法可以实现快速旋转目标的高分辨率ISAR成像。此外,可以有效解决MTRC和基础错配问题。 (c)2019年光学仪表工程师协会(SPIE)

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