The rotating micro-motion parts produce micro-Doppler (m-D) effects which severely influence the quality of inverse synthetic aperture radar (ISAR) imaging for complex moving targets. Recently, a method based on short-time Fourier transform (STFT) and L-statistics to remove m-D effects is proposed, which can separate the rigid body parts from interferences introduced by rotating parts. However, during the procedure of removing m-D parts, the useful data of the rigid body parts are also removed together with the m-D interferences. After summing the rest STFT samples, the result will be affected. A novel method is proposed to recover the missing values of the rigid body parts by the particle swarm optimization (PSO) algorithm. For PSO, each particle corresponds to a possible phase estimation of the missing values. The best particle is selected which has the minimal energy of the side lobes according to the best fitness value of particles. The simulation and measured data results demonstrate the effectiveness of the proposed method.

中文翻译：

---

雷达分析中微多普勒去除后刚体重建的粒子群优化

旋转的微运动部件产生微多普勒（mD）效应，严重影响复杂运动目标的逆合成孔径雷达（ISAR）成像质量。最近，提出了一种基于短时傅里叶变换 (STFT) 和 L 统计量的去除 mD 效应的方法，该方法可以将刚体部件与旋转部件引入的干涉分开。然而，在去除mD零件的过程中，刚体零件的有用数据也随着mD干涉一起被去除。将其余 STFT 样本相加后，结果将受到影响。提出了一种通过粒子群优化（PSO）算法恢复刚体部分缺失值的新方法。对于 PSO，每个粒子对应于缺失值的可能相位估计。根据粒子的最佳适应值选择具有最小旁瓣能量的最佳粒子。仿真和实测数据结果证明了该方法的有效性。