Radar Signal Sorting Method with Mimetic Image Mapping Based on Antenna Scan Pattern via SOLOv2 Network

Aiming at the problems, in which the traditional radar signal sorting method has high requirements for manual experience and poor adaptability, and considering the differences in received power caused by radar beam scanning under long-term observation, an end-to-end signal sorting method based on the instance segmentation network SOLOv2 and using an antenna scan pattern (ASP) is proposed in this letter. Firstly, the interleaved pulse sequences of multiple radar signals with various inter-pulse modulation types, scan patterns, and gain patterns are simulated, mimetic image mapping is constructed to visualize the interleaved pulse sequences as mimetic point graphs, and the index relationship between pulses and pixel points is recorded. Subsequently, the SOLOv2 instance segmentation network is used to segment the mimetic point graph at the pixel level, thereby clustering the discrete pixel points in the image. Finally, based on the index relationship recorded during the construction of the mimetic image mapping, the clustering results of points in the image are traced back to the clustering of pulses, achieving end-to-end intelligent radar signal sorting. Through simulation experiments, it was verified that, compared with YOLOv8-based, U-Net-based, and traditional signal sorting methods, the sorting accuracy of the proposed method increased by 9.26%, 11.17%, and 24.55% in the scenario of five signals with 30% missing pulse ratio (MPR), and increased by 13.33%, 18.88%, and 23.94% in the scenario of five signals with 30% spurious pulse ratio (SPR), respectively. The results show that by introducing the stable parameter, namely ASP, the proposed method can achieve signal sorting with highly overlapping parameters and adapt to non-ideal conditions with measurement errors, missing pulses, and spurious pulses.