To address the issues of incomplete data cleaning mechanisms for specific emitter signals and insufficient adaptive feature representation capability leading to low individual recognition rates， this paper proposes an individual identification algorithm incorporating sample alignment and a semi-global attention mechanism. During the data preprocessing stage， the main components of specific emitter intermediate frequency （IF） signals are extracted through multi-condition dual-threshold processing， enabling arrival time synchronization across samples. Subsequently， multi-dimensional feature alignment operations， including pulse width alignment and amplitude normalization， are performed， ensuring the cleaned samples gain generalization capability for individuals with identical frequency points. A semi-global attention mechanism is introduced and integrated with a dual-channel convolutional neural network （CNN）， strengthening the network's feature representation capacity while maintaining training efficiency. This mechanism combines semi-global geometric similarity with learnable similarity measures， employing parallel processing of sparse attention scores to preserve long-range feature dependencies， thereby achieving more accurate feature details and higher computational efficiency. Experimental results on measured specific emitter signal datasets indicate that the proposed method effectively address the challenges of uneven data distribution and inadequate adaptive feature representation， significantly improving model recognition accuracy.
