Open-Set UAV Signal Identification Using Learnable Embeddings and Energy-Based Inference

Reliable recognition of unmanned aerial vehicle (UAV) communication signals is essential for low-altitude airspace safety and UAV monitoring. In practical electromagnetic environments, UAV signals exhibit complex time-frequency characteristics, and unknown signal types frequently appear, making open-set recognition necessary. This paper proposes a geometry-energy open-set recognition (GE-OSR) method for UAV signal identification. First, a time-frequency convolutional hybrid network is developed to learn multi-scale representations from raw UAV signals. Then, learnable class embeddings with a dual-constraint embedding loss are introduced to improve feature compactness and separability. In addition, a free-energy alignment loss is introduced to assign low energy to known signals and high energy to unknown ones, forming an adaptive rejection boundary. Experiments under different signal-to-noise ratios (SNRs) and openness levels show that GE-OSR provides stable performance. At 0 dB SNR under high openness, the method improves OSCR by about 2.95% over the recent S3R model and more than 6% over other baselines. These results show that GE-OSR is effective for practical UAV signal identification and unknown signal detection in complex low-altitude environments.