We propose a drone signal out-of-distribution (OOD) detection algorithm based on discriminability-driven spatial-channel selection with a gradient norm. Time-frequency image features are adaptively weighted along both spatial and channel dimensions by quantifying inter-class similarity and variance based on protocol-specific time-frequency characteristics. Subsequently, a gradient-norm metric is introduced to measure perturbation sensitivity for capturing the inherent instability of OOD samples, which is then fused with energy-based scores for joint inference. Simulation results demonstrate that the proposed algorithm provides superior discriminative power and robust performance via SNR and various drone types.
@article{arxiv.2601.18329,
title = {Discriminability-Driven Spatial-Channel Selection with Gradient Norm for Drone Signal OOD Detection},
author = {Chuhan Feng and Jing Li and Jie Li and Lu Lv and Fengkui Gong},
journal= {arXiv preprint arXiv:2601.18329},
year = {2026}
}