Work

Adaptive UAV Navigation Fusion with Scene Matching and Mixed Credibility UKF

Year: 2026

Type: article

Abstract: To address the degradation of unmanned aerial vehicle (UAV) positioning accuracy caused by variations in sensor performance under changing environments, a scene-matching-based multi-sensor fusion algorithm, termed mixed credibility unscented Kalman filter (MCUKF), is proposed. The method jointly considers environmental and motion scenes for adaptive sensor selection, evaluates data credibility using generalized and narrow Jaccard coefficients, and employs an improved AdaBoost classifier for intelligent scene recognition. By combining filtering credibility and data credibility, the proposed MCUKF enables adaptive fusion of multi-sensor data. Experimental results from simulations and real flight tests demonstrate its effectiveness. In the adaptive sensor fusion experiment, the proposed method achieves an RMSE of 0.1333[Formula: see text]m, compared with 0.6403[Formula: see text]m for the Global Positioning System (GPS), 0.7697[Formula: see text]m for the camera, and 0.4873[Formula: see text]m for direct GPS-camera fusion. In comparison with representative fusion algorithms, MCUKF attains an RMSE of 0.1806[Formula: see text]m, outperforming MMSE (0.5140[Formula: see text]m), EUKF (1.3326[Formula: see text]m), and STETFF (0.9568[Formula: see text]m). These results demonstrate the superiority of the proposed method in adaptive sensor selection and robust fusion during scene transformation.

Source: Guidance Navigation and Control

DOI: 10.1142/s2737480726400091

Authors: Peng He, Jutao Wang, Quanbo Ge, Zheng Wang, Sheng Chen, Yang Liu

Institutions: Nanjing University of Information Science and Technology, Jiangsu Ocean University, Zhejiang Normal University

Language: English

Topic: Inertial Sensor and Navigation

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond