A Radar Signal Open-Set Deinterleaving Method Based on Laplacian Pyramid Reconstruction and Adversarial Reciprocal Point Learning

Radar signal deinterleaving (RSD) is a challenging task in complex electromagnetic environments. This work proposes a method for accurately deinterleaving known and unknown radar radiation sources in open space using an open set deinterleaving technique for radar signals based on the combination of Laplace pyramid reconstruction and reciprocal point adversarial deinterleaving (PR-RPAD). The Pulse Description Graph (PDG) representation of the intercepted PDW is first realized by applying a gray matrix symmetric mapping approach based on sliding windows. Second, to improve the visual characterization of the pulse description information of various radar radiation sources, PDG edges and texture structures are amplified and enhanced using the Otsu threshold-based image amplification technique. Next, a Laplace pyramid-based multi-resolution feature reconstruction and fusion model is put forth, which uses a jump connection and a multiplication gate to achieve the tower reshaping from low-resolution to high-resolution features. The idea of reciprocal points is finally introduced to model the open space to decrease the risk of closed set deinterleaving through confrontation with known radars. To improve the discriminatory nature of the model against unknown radars and to finish the accurate deinterleaving of radar radiation sources in the open space, an instantiated confrontation enhancement method is used to generate confusing samples for training. In addition to having better performance in closed-set deinterleaving than other deinterleaving techniques (SDIF, PRI-Tran, BLSTM, BGRU, and DCN), the PR-RPAD method also achieves deinterleaving in open space, which offers great versatility and potential for real-world use in intricate electromagnetic environments.