A Multi-alignment Task-adaptive Method for MFR Mode Recognition on Few-shot Open-set Learning

Multi-function radars (MFRs) can perform multiple missions simultaneously by optimizing transmission signals using programmable parameters, which is a great challenge for reconnaissance and identification. In particular, this recognition becomes more challenging when there is no prior information on radiation sources and when the available labeled signal data is insufficient. The received signal is also often a mixture of signals from targets, noise, unknown radiation sources, or unknown working modes. This paper proposes a Multi-alignment Taskadaptive (MaTa) method to simultaneously complete the detection of unknown signals and the classification of target mode signals with limited samples. The proposed method utilizes generative model to map the observed signal sample and its semantic descriptions of the working mode to the same latent variables space through multi-alignment. Each working mode generates a prototype using a small number of projections in this space to support classification. This paper additionally generates negative prototypes without unknown signal sample provided to meet the requirement of dynamic adjusting the detection boundary in different tasks for unknown samples. The proposed method shows the best experimental results compared with baselines, which achieves a 96.73% accuracy for 5-categories providing one sample per class under few-shot open-set learning condition.