@article{ZHAO2026, 
author = {Guiling ZHAO and Jinbao WANG and Zihao JIANG and Shuai GAO},
title = {A GNSS/SINS fault detection and robust adaptive algorithm based on two parameters},
year = {2026},
journal = {Journal of Beijing University of Aeronautics and Astronautics},
volume = {52},
number = {3},
pages = {655-667},
keywords = {integrated navigation, fault detection, robust adaptive algorithm, innovation residual, width of smooth bounded layer},
url = {https://www.sciopen.com/article/10.13700/j.bh.1001-5965.2023.0822},
doi = {10.13700/j.bh.1001-5965.2023.0822},
abstract = {The global navigation satellite system (GNSS) signal is susceptible to interference, which leads to a decrease in filter performance and affects the output accuracy of the global navigation satellite system/strapdown inertial navigation system (GNSS/SINS) integrated navigation system. For this problem, this paper proposes a GNSS/SINS fault detection and robust adaptive algorithm based on two parameters. For precise fault detection, the algorithm creates a fault detection function based on the breadth of the smooth bounded layer and assesses the system measurement data using the innovation residual. Using fault detection function values to construct the robust cofactor matrix for real-time error correction to improve the accuracy and robustness of state estimation. The experimental findings of two common GNSS faults, step fault and slow fault, demonstrate that: the robust adaptive algorithm and the GNSS/SINS fault detection method based on two parameters are contrasted with the conventional robust adaptive algorithm based on residual chi-square fault detection techniques. The velocity fault detection rate is increased by 7.6%−23.2%, and the position fault detection rate is increased by 3.2%−12.3%. The velocity accuracy is increased by 20.7%−27.1%, and the position accuracy is increased by 22.2%−34.6%. The algorithm effectively improves the accuracy and robustness of the GNSS/SINS integrated navigation system.}
}