Abstract: ‍ ‍In the field of Inverse Synthetic Aperture Radar （ISAR） imaging， motion compensation is a critical step to ensure high-quality image generation. Range alignment （RA） serves as the primary step in motion compensation， correcting range offsets in echo signals caused by translational motion. This article introduces a new range alignment method based on a Complex-Valued Convolutional Neural Network （CV-CNN）， aiming to enhance the accuracy and computational efficiency of range alignment through deep learning techniques. The proposed method leverages the potent feature-learning capabilities of convolutional neural networks to construct a model that maps the complex relationship between one-dimensional distance profiles and range compensation amounts. By extending traditional real-valued convolutional networks into the complex domain， our approach preserves the phase information in the echo signal and effectively incorporates residual blocks and linear connection mechanisms in the complex domain， refining the network structure design. These architectural improvements allow the proposed algorithm to achieve efficient range alignment of ISAR range profiles， even under low signal-to-noise ratio （SNR） conditions. For data generation， an ISAR echo dataset was constructed through simulation based on relevant radar parameters. After normalization， this dataset was input into the network for training， enabling it to learn the mapping from unaligned echoes to the corresponding compensation quantities. The method employs a transfer learning strategy to fine-tune the pre-trained model on simulation data， adapting it to actual measurement data. This strategy enhances the reliability of the results and significantly shortens the model’s iteration cycle. In terms of experimental verification， the article rigorously evaluates the algorithm’s effectiveness using both simulated and measured data， focusing on range alignment accuracy， imaging result quality， and computational efficiency as key indicators. The experimental results demonstrate that， under various signal-to-noise ratio conditions， the method exhibits exceptional envelope alignment performance， yielding high-quality imaging and substantial advantages in computational efficiency.