A Review of GNSS Spoofing Detection Based on Multi-Source Fusion

Due to the weak signal power and the publicly known structure of civil signals, Global Navigation Satellite Systems (GNSSs) face an increasingly severe threat from spoofing interference. Constrained by inherent limitations such as physical boundaries and the inability of self-validation, single-source detection methods struggle to effectively counter complex spoofing attacks. Consequently, the advantages of multi-source fusion detection have become increasingly prominent. By incorporating heterogeneous sensors such as Inertial Navigation System (INS), vision, and communication signals, multi-source fusion technology leverages independent reference sources for cross-validation, providing an effective approach to overcome the bottlenecks of single-source detection. This paper systematically reviews 67 representative publications in the field of multi-source fusion GNSS spoofing detection from 2021 to 2026. Existing methods are categorized into three main classes: filtering algorithm-based detection, multi-source data consistency-based detection, and Artificial Intelligence (AI)-based detection. The advantages and limitations of each category are systematically compared, and the current research status along with future development trends are summarized.