Radar Signal Sorting With Multiple Self-Attention Coupling Mechanism Based Transformer Network
Zixiang Zhou, Xiongjun Fu, Jian Dong, Meijing Gao
Abstract
In modern electromagnetic countermeasure environments, traditional radar signal sorting (RSS) methods face challenges from incompletely intercepted parameter-dense pulses of multi-function radars (MFRs). To cope with this situation, this letter proposes a sequence-to-sequence RSS method based on a multiple self-attention coupling mechanism Transformer network. The method utilizes positional encoding to obtain stable temporal information. A multiple self-attention coupling mechanism is then designed to calculate the attention matrix, thereby extracting sequence relationships for the non-ideal pulse stream. Finally, a decoder network is employed to extract high-dimensional features and translate the corresponding labels for each pulse. Simulation experiments demonstrate that compared with some existing methods, the proposed method can achieve better average sorting accuracy with little computational cost under the conditions of overlapping parameters, limited label, missing pulses, and various modulation types of intercepted MFR signals.