Abstract: ‍ ‍Range alignment is the first step of translational compensation processing for inverse synthetic aperture radar （ISAR） imaging， and the accuracy of range alignment has a notable impact on the quality of azimuth focusing and final imaging. To solve the problem of the serious impairment of the performance of traditional range alignment algorithms under the condition of sparse aperture and low signal-to-noise ratio （SNR）， a novel range alignment method based on a spatiotemporal attention- sequence-to-sequence （Seq2Seq） network is proposed. A gated recurrent unit （GRU） was adopted in this model as the encoding and decoding unit. The spatial attention mechanism was modified according to the unique energy distribution characteristics of the range profile of point-targets. The ability to align the ISAR range profile was finally formed by incorporating the temporal and spatial attention mechanism. For training data generation， an ISAR echo dataset was constructed through imaging simulation based on electromagnetic wave simulation parameters and target motion simulation parameters. After 8-fold interpolation， it was input into the network for training， allowing the network to learn the mapping relationship from unaligned echoes to aligned echoes. The proposed method replaced online correlation calculations with offline training. By integrating the advantages of the Seq2Seq network model in handling Seq2Seq problems， the advantages of the temporal attention mechanism in capturing long-term dependencies， and the advantages of the spatial attention mechanism in extracting regional features， the proposed method achieved automatic alignment of ISAR echoes in the range slow-time domain under sparse aperture and low SNR conditions. By inputting unaligned echo sequences into the trained spatiotemporal attention-Seq2Seq network， range alignment could be automatically achieved without changing the echo phase structure. Simulation and experimental data show that， compared with traditional range alignment methods， the proposed method obtained better alignment accuracy under sparse aperture and low SNR conditions. A range alignment experiment was performed using measured echo data for the Yak-42 aircraft under the conditions of a 50% under-sampling rate and a 0 dB signal-to-noise ratio. The cyclic shift error was reduced from 39 and 26 to 6， and the image entropy of the imaging results was reduced from 4.58 and 4.22 to 1.71 using the proposed method， verifying its good performance.