The near-field array synthetic aperture radar (SAR) imaging method that’s based on deep neural networks has significantly advanced imaging accuracy and efficiency compared to traditional techniques like matched filtering and sparse reconstruction. However, it currently relies on supervised learning, which is affected by differences between simulated and real data. To address the issue, we introduces an unsupervised approach based on generative models of latent variables for near-field array SAR imaging. By focusing on generating target image distributions, this method bypasses the need for simulated training data. Instead, it leverages the concept of generative models with latent variables, using a prior auxiliary variable to construct a decoding neural network that transforms these latent variables into target images. In addition, a model-driven loss function is designed based on physical priors related to the linear correspondence between echoes and target images in the SAR measurement process.To enhance image quality further, sparse constraints (L1) loss function is integrated into the approach’s final loss function. Experimental validation using actual millimeter-wave near-field array SAR data demonstrates the effectiveness of this unsupervised imaging method. It offers advantages such as not relying on simulated data for training, suitability for diverse target types, superior imaging accuracy compared to traditional methods, and the ability to maintain high accuracy even at low sampling rates (10%).