Zhang Zhichao, Deng Zhongliang, Liu Jingrong, Ding Zhenke, Liu Bingxun
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel). 2024 May 21;24(11):3266. doi: 10.3390/s24113266.
Global Navigation Satellite Systems (GNSS) offer comprehensive position, navigation, and timing (PNT) estimates worldwide. Given the growing demand for reliable location awareness in both indoor and outdoor contexts, the advent of fifth-generation mobile communication technology (5G) has enabled expansive coverage and precise positioning services. However, the power received by the signal of interest (SOI) at terminals is notably low. This can lead to significant jamming, whether intentional or unintentional, which can adversely affect positioning receivers. The diagnosis of jamming types, such as classification, assists receivers in spectrum sensing and choosing effective mitigation strategies. Traditional jamming diagnosis methodologies predominantly depend on the expertise of classification experts, often demonstrating a lack of adaptability for diverse tasks. Recently, researchers have begun utilizing convolutional neural networks to re-conceptualize a jamming diagnosis as an image classification issue, thereby augmenting recognition performance. However, in real-world scenarios, the assumptions of independent and homogeneous distributions are frequently violated. This discrepancy between the source and target distributions frequently leads to subpar model performance on the test set or an inability to procure usable evaluation samples during training. In this paper, we introduce LJCD-Net, a deep adversarial migration-based cross-domain jamming generalization diagnostic network. LJCD-Net capitalizes on a fully labeled source domain and multiple unlabeled auxiliary domains to generate shared feature representations with generalization capabilities. Initially, our paper proposes an uncertainty-guided auxiliary domain labeling weighting strategy, which estimates the multi-domain sample uncertainty to re-weight the classification loss and specify the gradient optimization direction. Subsequently, from a probabilistic distribution standpoint, the spatial constraint imposed on the cross-domain global jamming time-frequency feature distribution facilitates the optimization of collaborative objectives. These objectives include minimizing both the source domain classification loss and auxiliary domain classification loss, as well as optimizing the inter-domain marginal probability and conditional probability distribution. Experimental results demonstrate that LJCD-Net enhances the recognition accuracy and confidence compared to five other diagnostic methods.
全球导航卫星系统（GNSS）在全球范围内提供全面的位置、导航和授时（PNT）估计。鉴于室内和室外环境中对可靠位置感知的需求不断增长，第五代移动通信技术（5G）的出现实现了广泛覆盖和精确的定位服务。然而，终端处感兴趣信号（SOI）接收到的功率明显较低。这可能导致严重的干扰，无论是有意还是无意的，都会对定位接收器产生不利影响。干扰类型的诊断，如分类，有助于接收器进行频谱感知并选择有效的缓解策略。传统的干扰诊断方法主要依赖分类专家的专业知识，通常对各种任务缺乏适应性。最近，研究人员开始利用卷积神经网络将干扰诊断重新概念化为图像分类问题，从而提高识别性能。然而，在实际场景中，独立同分布的假设经常被违反。源分布和目标分布之间的这种差异经常导致测试集上模型性能不佳，或者在训练期间无法获得可用的评估样本。在本文中，我们介绍了LJCD-Net，一种基于深度对抗迁移的跨域干扰泛化诊断网络。LJCD-Net利用完全标记的源域和多个未标记的辅助域来生成具有泛化能力的共享特征表示。首先，我们的论文提出了一种不确定性引导的辅助域标记加权策略，该策略估计多域样本不确定性以重新加权分类损失并指定梯度优化方向。随后，从概率分布的角度来看，对跨域全局干扰时频特征分布施加的空间约束有助于协作目标的优化。这些目标包括最小化源域分类损失和辅助域分类损失，以及优化域间边缘概率和条件概率分布。实验结果表明，与其他五种诊断方法相比，LJCD-Net提高了识别准确率和置信度。
