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.