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.

Worker location information is essential for ensuring safety in industrial environments. Fifth-generation (5G) technology’s wide coverage and comprehensive factory infrastructure make it crucial for industrial positioning. As the fundamental observation, current 5G ranging primarily relies on large bandwidth configurations, imposing stringent radio-frequency requirements and high power consumption that conflicts with the extended battery life needed for industrial devices. To address this limitation, we propose a frequency-enhanced reference signal (FERS) that enables high-precision ranging under industrial hardware constraints while maintaining low power consumption and limited bandwidth requirements. Furthermore, we develop and integrate a fine phase ranging (FPR) algorithm to achieve super-resolution distance measurement. Simulation and experimental results demonstrate that the proposed approach significantly enhances ranging accuracy under low-bandwidth constraints. The proposed FPR algorithm delivers superior accuracy while maintaining computational complexity that is an order of magnitude lower than state-of-the-art super-resolution ranging methods. Additionally, the approach exhibits strong robustness under low signal-to-noise ratio conditions.