Research on the SSUKF Integrated Navigation Algorithm Based on Adaptive Factors - Zhongliang Deng; Yanlin Zhang; Yanbiao Gao (Applied Sciences)
In complex environments, the application of traditional Kalman Filtering in GNSS/INS integrated navigation systems often encounters challenges such as filter divergence and accuracy degradation. This paper introduces the technique of Simplified Spherical Unscented Kalman Filtering (SSUKF) and, based on this, proposes an Adaptive Simplified Spherical Unscented Kalman Filtering (ASSUKF) integrated navigation method. This approach, built upon SSUKF, incorporates an adaptive filter that effectively utilizes residuals and innovation sequences to mitigate the divergence phenomenon during the filtering process. Furthermore, the system is capable of online estimation and dynamic adjustment of the statistical characteristics of measurement noise, leading to more accurate state estimation and significantly enhancing the adaptive capability of SSUKF. ASSUKF improves position accuracy in the latitude direction by 18.10% and in the longitude direction by 20.6%. For attitude error, ASSUKF performs exceptionally well. Specifically, the pitch angle error improves by 27.6% compared to UKF and by 27.1% compared to SSUKF. The roll angle error improves by 29.9% compared to UKF and by 20.1% compared to SSUKF. The heading angle error improves by 24.3% compared to SSUKF, validating the method’s substantial advantages in improving system accuracy and robustness, demonstrating its effectiveness and potential in complex environments.