The rapid expansion of non-geostationary orbit (NGSO) satellites alongside existing geostationary orbit (GSO) systems has intensified spectrum congestion and inter-system interference, placing stringent demands on real-time interference management to sustain reliable coexistence in next-generation communication networks. While existing machine learning (ML)-based reconstruction models have made strides, they remain constrained to an area under the curve (AUC) of 0.83 due to fixed thresholds, causing unacceptable false alarm rates that undermine critical link reliability. Additionally, their decoupled training paradigm neglects cross-domain dependencies, limiting time and frequency-domain AUCs to 0.83 and 0.71, respectively. To address these limitations, this paper introduces a semi-supervised satellite interference detection framework named Agon, employing a novel two-stage hybrid learning paradigm. Agon integrates masked autoencoder (MAE) pre-training of a dual attention transformer (DAT) with multi-task fine-tuning to optimize a direct binary classifier, effectively eliminating unstable thresholds. Furthermore, it incorporates high-order statistics (HOS)-augmented attention and wavelet regularization to bolster noise robustness and structural fidelity. Extensive validation on public NGSO-GSO dataset and a high-fidelity NGSO-NGSO dataset demonstrates that Agon achieves state-of-the-art (SOTA) detection performance, with a 25.3% improvement in AUC. Moreover, the multi-task learning (MTL) framework facilitates accurate modulation classification with accuracies exceeding 90%, while simultaneously maintaining optimal detection performance across diverse scenarios characterized by varying off-axis angles and interference-to-noise ratios (INRs).

翻译：非地球静止轨道（NGSO）卫星与现有地球静止轨道（GSO）系统的快速扩张加剧了频谱拥塞和系统间干扰，对下一代通信网络中实现可靠共存所需的实时干扰管理提出了严格要求。尽管基于机器学习（ML）的重构模型已取得进展，但由于固定阈值的限制，其曲线下面积（AUC）仍局限于0.83，导致不可接受的虚警率，从而损害了关键链路的可靠性。此外，其解耦训练范式忽略了跨域依赖关系，使得时域和频域的AUC分别限制在0.83和0.71。为解决这些局限，本文提出了一种名为Agon的半监督卫星干扰检测框架，采用新颖的两阶段混合学习范式。Agon将掩码自编码器（MAE）预训练的双注意力Transformer（DAT）与多任务微调相结合，以优化直接二分类器，从而有效消除不稳定阈值。此外，它引入高阶统计量（HOS）增强的注意力机制和小波正则化，以增强噪声鲁棒性和结构保真度。在公开的NGSO-GSO数据集和高保真NGSO-NGSO数据集上的广泛验证表明，Agon实现了最先进的检测性能，AUC提升了25.3%。同时，多任务学习（MTL）框架促进了准确的调制分类，准确率超过90%，并在不同离轴角度和干扰噪声比（INR）表征的多种场景中保持最优检测性能。