GNSS are indispensable for various applications, but they are vulnerable to spoofing attacks. The original receiver autonomous integrity monitoring (RAIM) was not designed for securing GNSS. In this context, RAIM was extended with wireless signals, termed signals of opportunity (SOPs), or onboard sensors, typically assumed benign. However, attackers might also manipulate wireless networks, raising the need for a solution that considers untrustworthy SOPs. To address this, we extend RAIM by incorporating all opportunistic information, i.e., measurements from terrestrial infrastructures and onboard sensors, culminating in one function for robust GNSS spoofing detection. The objective is to assess the likelihood of GNSS spoofing by analyzing locations derived from extended RAIM solutions, which include location solutions from GNSS pseudorange subsets and wireless signal subsets of untrusted networks. Our method comprises two pivotal components: subset generation and location fusion. Subsets of ranging information are created and processed through positioning algorithms, producing temporary locations. Onboard sensors provide speed, acceleration, and attitude data, aiding in location filtering based on motion constraints. The filtered locations, modeled with uncertainty, are fused into a composite likelihood function normalized for GNSS spoofing detection. Theoretical assessments of GNSS-only and multi-infrastructure scenarios under uncoordinated and coordinated attacks are conducted. The detection of these attacks is feasible when the number of benign subsets exceeds a specific threshold. A real-world dataset from the Kista area is used for experimental validation. Comparative analysis against baseline methods shows a significant improvement in detection accuracy achieved by our Gaussian Mixture RAIM approach. Moreover, we discuss leveraging RAIM results for plausible location recovery.

翻译：全球导航卫星系统（GNSS）在各类应用中不可或缺，但其易受欺骗攻击。传统接收机自主完好性监测（RAIM）并非为保障GNSS安全而设计。在此背景下，RAIM通过无线信号（即机会信号，SOP）或通常假设可信的机载传感器进行扩展。然而，攻击者亦可能操纵无线网络，因此需要一种考虑不可信机会信号的解决方案。为此，我们通过融合所有机会信息（即来自地面基础设施和机载传感器的测量值）扩展RAIM，形成针对GNSS欺骗攻击的鲁棒检测统一功能。其目标是通过分析扩展RAIM解算的位置（包括来自GNSS伪距子集和不可信网络无线信号子集的位置解）来评估GNSS欺骗的可能性。该方法包含两个核心组件：子集生成与位置融合。我们生成测距信息子集并通过定位算法处理，产生临时位置；机载传感器提供速度、加速度和姿态数据，基于运动约束辅助位置滤波。经不确定性建模的滤波位置被融合为归一化的复合似然函数，用于GNSS欺骗检测。针对非协调与协调攻击场景，开展了仅依赖GNSS及多基础设施场景的理论评估。当良性子集数量超过特定阈值时，此类攻击可被检测。利用基斯塔地区的真实数据集进行实验验证，与基线方法对比表明，我们的高斯混合RAIM方法显著提升了检测精度。此外，我们探讨了利用RAIM结果实现合理位置恢复的可能性。