RESeTNet: Residual Embedding Sequential Transformer Network for High-Repetition Pulse Deblurring in Passive Location

Pulse deblurring is a key technology for passive localization of reconnaissance signals, and the performance of pulse deblurring directly affects the accuracy of time difference of arrival (TDOA) localization. Under the condition of high pulse repetition frequency (HPRF), traditional pulse deblurring methods face significant challenges in terms of accuracy and efficiency. In response to the aforementioned issues, this letter proposes a high-repetition pulse deblurring method based on a residual embedding sequential transformer network (RESeTNet). First, based on the ideal passive localization scenario and TDOA model, the pulse separation ambiguity problem under high-repetition frequency is analyzed. The TDOA deblurring problem is modeled as a pulse classification problem under a fixed category set. Second, by improving existing classification network models, the RESeTNet model and a deblurring method based on RESeTNet are proposed. Finally, model validation was conducted using simulated data from STK software. The results indicate that the proposed method can significantly improve the accuracy of pulse deblurring under high repetition frequency conditions, achieving a 97.5% unambiguous localization probability even under high repetition rate conditions of 600 kHz.