Satellite-Aided Multi-UAV Secure Collaborative Localization via Spatio-Temporal Anomaly Detection and Diagnosis

Highlights: What are the main findings? In a malicious cooperative detection environment, leveraging the evolution patterns of spatio-temporal correlations among ranging sequences across timestamps improves the anomaly detection accuracy to 96.15%. By eliminating abnormal sequences and correcting them using a K-nearest-neighbor-based imputation method, the localization accuracy increases by 25.9%. What is the implication of the main finding? Satellites can effectively assist UAV swarms in detecting emitters over wide areas, even when satellite-to-UAV links experience significant delays. Although malicious information in multi-UAV collaborative detection can severely degrade system performance, artificial intelligence-based detection and repair mechanisms can effectively mitigate these effects and restore accuracy. Satellite-aided multi-unmanned aerial vehicle (UAV) collaborative localization systems combine the extensive coverage of satellites with the flexibility of UAVs, offering new opportunities for locating highly dynamic emitters across large areas. However, the openness of space-air communication links and the increasing complexity of cybersecurity threats make these systems vulnerable to false data injection attacks. Most existing detection approaches focus only on temporal dependencies in time-frequency features and lack diagnostic mechanisms for identifying malicious UAVs, which limits their ability to effectively detect and mitigate such attacks. To address this issue, this paper proposes an intelligent collaborative localization framework that safeguards localization integrity by identifying and correcting false ranging information from malicious UAVs. The framework captures spatio-temporal correlations in multidimensional ranging sequences through a graph attention network (GAT) coupled with a time-attention-based variational autoencoder (VAE) to detect anomalies through anomalous distribution patterns. Malicious UAVs are further diagnosed through an anomaly scoring mechanism based on statistical analysis and reconstruction errors, while detected anomalies are corrected via a K-nearest neighbor-based (KNN) algorithm to enhance localization performance. Simulation results show that the proposed model improves localization accuracy by 25.9%, demonstrating the effectiveness of spatial–temporal feature extraction in securing collaborative localization.