通过梯度范数与特征选择提升无人机信号异常检测精度
Discriminability-Driven Spatial-Channel Selection with Gradient Norm for Drone Signal OOD Detection
- 基于时频特征自适应加权空间与通道维度
- 梯度范数捕捉异常样本的内在不稳定性，显著提升区分度
- 适用于多种无人机类型和信噪比条件，鲁棒性强
我们提出一种基于判别性驱动的空间-通道选择与梯度范数的无人机信号分布外（OOD）检测算法。该方法依据特定协议的时频特性，量化类间相似性与方差，对时频图像特征在空间和通道维度上进行自适应加权。随后引入梯度范数度量扰动敏感性，以捕获OOD样本的内在不稳定性，并将其与能量分数融合用于联合推断。仿真结果表明，所提算法在不同信噪比（SNR）和多种无人机类型下均展现出优异的判别能力与鲁棒性能。
原文摘要 · Abstract (English)
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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