Abstract: When millimeter-wave synthetic aperture radar (SAR) images near-field targets, the selection of the distance gate where the target is located directly affects the imaging effect. In addition, the echo signal of the radar in the near field will be modulated by high sidelobes, and the echo information at the edge of the target will be masked, resulting in blurred edge imaging. To solve the above problems, this paper proposes an adaptive matching filtering near-field imaging algorithm that combines simulated annealing algorithm (SA) and particle swarm optimization (PSO). Firstly, the target distance, Kaiser window parameters and interpolation factors were introduced as the three-dimensional solutions of particle position parameters, and a multi-dimensional weighted fitness function based on the fusion peak signal-to-noise ratio, contrast and image entropy was constructed for optimization iteration. Secondly, in order to solve the problem that particle swarms are easy to fall into local solutions, the SA algorithm is introduced to dynamically adjust the weight ratio of the multi-dimensional fitness function in the iterative process to determine the optimal parameter combination. Finally, based on the optimal parameter combination, the relationship between the interpolation factor and the size of the Lee filter window was established, and the window size was adaptively adjusted for noise suppression, so as to realize the optimal matching filter imaging. Experiments show that the proposed method can effectively improve the imaging resolution of near-field SAR imaging, improve the edge information, and obtain high-quality imaging results.