Robust Unscented Kalman Filtering With Measurement Error Detection for Tightly Coupled INS/GNSS Integration in Hypersonic Vehicle Navigation

• Main Authors: Gaoge Hu, Bingbing Gao, Yongmin Zhong, Longqiang Ni, Chengfan Gu
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: INS/GNSS integration; robust unscented Kalman filter; measurement errors; hypersonic vehicle navigation
• Online Access: https://ieeexplore.ieee.org/document/8876594/

Due to the high maneuverability of a hypersonic vehicle, the measurements for tightly coupled INS/GNSS (inertial navigation system/global navigation satellite system) integration system inevitably involve errors. The typical measurement errors include outliers in pseudorange observations and non-Gaussian noise distribution. This paper focuses on the nonlinear state estimation problem in hypersonic vehicle navigation. It presents a new innovation orthogonality-based robust unscented Kalman filter (IO-RUKF) to resist the disturbance of measurement errors on navigation performance. This IO-RUKF detects measurement errors by use of the hypothesis test theory. Subsequently, it introduces a defined robust factor to inflate the covariance of predicted measurement and further rescale the Kalman gain such that the measurements in error are less weighted to ensure the filtering robustness against measurement errors. The proposed IO-RUKF can not only correct the UKF sensitivity to measurement errors, but also avoids the loss of accuracy for state estimation in the absence of measurement errors. The efficacy and superiority of the proposed IO-RUKF have been verified through simulations and comparison analysis.