Discriminability-Driven Spatial-Channel Selection with Gradient Norm for Drone Signal OOD Detection
Year: 2026
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Feng, Chuhan, Li, Jing, Li, Jie, Lv, Lu, Gong, Fengkui
Institution: Xidian University
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7125820282
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