Information fusion algorithm with Student’s t filtering framework
Xiao-hang WU1(),Ke-mao MA2,*()
1. Beijing Institute of Space Long March Vehicle, Beijing 100076, China 2. School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
Considering the problem of non-Gaussian measurement noises in multi-sensor system, an unscented quaternion filtering algorithm was designed based on Student's t distribution as a local filtering algorithm, by combining the Student’s t filtering framework with the characteristics of quaternion and calculating the Student’s t weighted integral function by unscented transformation. The optimal fusion weight coefficients were calculated by Lagrange multiplier method, and the local filtering results were fused by linear weighted fusion method. Quaternion was applied for simulation of the target attitude motion model. Three star sensors were used to observe the same target at the same time. The effectiveness of the proposed algorithm was verified through the comparison with the existing robust unscented Student's t filtering (RSTUF) algorithm. The simulation results show that the proposed distributed information fusion algorithm based on Student’s t filtering framework has higher estimation accuracy, convergence speed and numerical stability than RSTUF algorithm, which improves the estimation accuracy and fault tolerance of the algorithm by the complementarity of multi-observation information.
Xiao-hang WU,Ke-mao MA. Information fusion algorithm with Student’s t filtering framework. Journal of ZheJiang University (Engineering Science), 2020, 54(3): 581-588.
Fig.1Frame diagram of star sensor/inertial measurement unit（IMU）integrated navigation system
Fig.2Curve for angle estimated error of cubature quaternion distribution fusion（CQDF）
Fig.3Curve for angle estimated error of robust Student’s t unscented filter（RSTUF）
Fig.4Angle estimated error of Student’s t unscented quaternion distribution fusion（STUDF）
Fig.5Curve for bias estimated error of CQDF
Fig.6Curve for bias estimated error of RSTUF
Fig.7Curve for bias estimated error of STUDF
Fig.82-norm curve of angle estimation error
Fig.92-norm curve of bias estimation error
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