逆合成孔径激光雷达机动目标运动补偿成像算法

Inverse Synthetic Aperture Lidar Motion Compensation Imaging Algorithm for Maneuvering Targets

逆合成孔径激光雷达(ISAL)成像运动补偿中,包络对齐的精度直接影响了相位误差估计精度。当目标速度和加速度较大时,距离包络严重倾斜且相位误差较大,图像无法进行良好聚焦。针对上述问题,在高精度成像模型的基础上提出了一种基于Nelder-Mead单纯形法和粒子群优化的全局联合运动误差补偿算法。首先,利用单纯形法估计目标速度,完成包络对齐。然后,将包络对齐过程获得的目标速度作为相位误差估计中参数初始化的约束条件。最后,用粒子群优化算法对各运动参数进行全局搜索并得到最优解,实现高精度运动参数估计及高阶相位误差补偿,得到聚焦良好的二维图像。实验结果表明,本算法的参数估计误差主要分布在±0.2%以内,参数估计精度和抗噪声性能均优于传统ISAL成像算法。

The accuracy of envelope alignment of imaging motion compensation in inverse synthetic aperture lidar(ISAL) directly affects the accuracy of phase error estimation. When the velocity and acceleration of the target are large, the range envelope is severely skewed and the phase error is tremendous, making it impossible to focus the image well. To address the above problem, a global motion error compensation joint estimation algorithm based on Nelder-Mead simplex method and particle swarm optimization is proposed in this paper, which is on the basis of high precision imaging model. The algorithm first estimates the target velocity using the simplex method to realize the envelope alignment. Then, the target velocity obtained in the envelope alignment process is used as the constraints for the initialization of the phase error estimation. The particle swarm optimization algorithm is used to search the global optimal solution for each motion parameters. Finally, the estimation of high-precision motion parameters and compensation of high-order phase error are achieved. Meanwhile, the well-focused two-dimensional images are obtained. The experimental results show that the parameter estimation error of the algorithm is mainly distributed within ±0.2%, and the parameter estimation accuracy and noise immunity are superior to the traditional ISAL imaging algorithm.