Adaptive Method for Outlier Detection of GNSS/INS Positioning in Complex Environments

Objectives: In complex environments, global navigation satellite system (GNSS) signals are susceptible to interference, leading to the presence of outliers in positioning results. Enhancing the performance of GNSS/inertial navigation system (INS) integrated navigation effectively and accurately detecting outliers in positioning results are crucial indicators of system integrity. Methods: To address the issue of high false positive and false negative rates in current single-threshold detection methods, a novel approach involves constructing fuzzy logic membership functions based on outlier characteristics and three thresholds. After normalization and exponential weighted smoothing, a new detection metric is formed, and an adaptive outlier detection control criterion is designed. Results: The results demonstrate the effectiveness of the proposed method. It enhances the determination capability of detection metrics in overlapping areas, effectively reducing false positive rates exceeding 93% compared to traditional methods. Additionally, the method incorporates adaptive adjustment of the detection time window, rapid response speed, and high detection success rate exceeding 98%. Conclusions: This algorithm improves the ability to assess measurements in overlapping regions while incorporating the feature of adaptively adjusting the detection time window. Outliers are almost never missed, and it responds quickly to abnormal conditions after the recovery process, promptly releasing fault warnings. Overall, compared to conventional detection methods, this algorithm significantly improving the efficiency of outlier detection and enhancing the reliability of GNSS/INS integrated navigation positioning.