A Sequence-to-Sequence Model for Online Signal Detection and Format Recognition

Signal detection and format recognition are critical and challenging tasks across civil and military sectors. However, they often encounter signal truncation issues during online signal processing, resulting in inaccurate predictions due to incomplete features. To address this issue, we herein propose the signal detection mask long short-term memory (SDM-LSTM) network module. The SDM-LSTM module facilitates seamless transmission of sequential signal features among blocks, even in scenarios facing signal truncation. Our approach involves organizing online-acquired signal samples into short blocks and conducting signal feature extraction and prediction for each block. This methodology ensures that each block retains the cumulative features from preceding blocks, thereby maintaining the continuity of signal features. Consequently, signal truncation no longer induces feature incompleteness. The proposed model, leveraging the SDM-LSTM module, offers predictions for each block, aligning with the sequence-to-sequence prediction paradigm. The simulations and experimental results validate the efficacy of our model in addressing signal truncation and facilitating online signal processing.