Multisensor Plug-and-Play Navigation Based on Resilient Information Filter

Abstract

To improve the positioning accuracy and resilience of the multisensor integration, a resilient plug-and-play navigation method based on information filter (IF) is proposed in this article. As the dual form of Kalman filter (KF), IF turns the likelihood product into a sum, which can fully utilize asynchronous sensor measurements for fusion and realize plug-and-play navigation flexibly. Furthermore, a resilient factor based on the principle of chi-square test is implemented to adjust the sensor information, aiming to reduce the impact of faulty measurements under challenging scenarios. By conducting and analyzing the vehicle experiments in the urban environment, the proposed method shows better performance over traditional KF, with the root mean square (rms) error reduced from 9.13 to 3.28 m. Plug-and-play navigation achieves a 51.37% improvement in positioning accuracy by utilizing more suitable sensor measurements and propagation intervals, which can decrease the sensitivity to measurement noise and faults. The resilient factor directly addresses the faults themselves and improves the positioning performance by 26.13%, further enhancing the system’s resilience and robustness to faulty information. This resilient IF method is fully validated as effective for multisensor plug-and-play navigation.