Discriminability-Driven Spatial-Channel Selection with Gradient Norm for Drone Signal OOD Detection

• URL: http://arxiv.org/abs/2601.18329v1
• Date: Mon, 26 Jan 2026 10:13:07 GMT
• Title: Discriminability-Driven Spatial-Channel Selection with Gradient Norm for Drone Signal OOD Detection
• Authors: Chuhan Feng, Jing Li, Jie Li, Lu Lv, Fengkui Gong,
• Abstract summary: We propose a drone signal out-of-distribution (OOD) detection algorithm based on discriminability-driven spatial-channel selection with a gradient norm.<n>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.
• Score: 12.735651073414452
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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