Robust Sequential Integrity Monitoring for Positioning Safety in GNSS/INS Integration
Jianbo Shao, Fei Yu, Ya Zhang, Qian Sun, Yanyan Wang, Wu Chen
Abstract
Integrity quantifies the confidence level in the position solution and is essential for positioning safety-critical applications. To monitor the integrity with a protection level (PL) of the multiple fault biases for a sequential filtering framework in challenging environments, a novel robust sequential IM approach is proposed. First, the impact of estimation consistency on PL is analyzed theoretically, and a front-end Student’s t distribution-based filter variant is adopted to provide consistent posterior estimates for constructing dynamic IM regression models and suppressing outliers. Then, under the multiple fault biases assumption, a maximum eigenvalue-based PL is calculated in a sequential filtering framework. Finally, two GNSS/INS in-vehicle experiments are conducted to validate the proposed method. The results indicate that the proposed method has a higher PL reliability (100% and 98.13%) than other methods, and did not suffer any hazardous misleading cases during the experiment. Therefore, the proposed method can assess the confidence of the position estimation of GNSS/INS and effectively monitor position integrity in challenging environments.