A 5G Adaptive Positioning Method in Environments with Significant Noise Changes

Yingzhe He, Chao Sun, Lu Bai, Wenquan Feng, Shuai Zhang, Syed Shahid Shah

Peer Reviewed

Abstract:	As the demand for positioning grows, precision and reliability are becoming increasingly important. While Global Navigation Satellite Systems (GNSS) offer outdoor positioning, their limitations in urban settings necessitate alternative solutions. 5G positioning emerges as a complementary approach, leveraging disruptive technologies like millimeter-wave and massive MIMO antennas, enabling high precision. Current 5G positioning methods, mainly Extended Kalman Filter (EKF)-based, suffer from time-varying noise in 5G measurements, degrading accuracy. To overcome this, adaptive methods like Adaptive Kalman Filter (AKF) have been introduced, but suboptimal performance persists. This work introduces Variational Bayesian Adaptive Kalman Filter (VBAKF) for 5G positioning. The work includes simulation verification, VBAKF performance evaluation under varying ƛ factors, and comparative analyses with conventional adaptive methods. Simulations reveal VBAKF's superior performance, positioning it as an effective choice for 5G positioning, especially in challenging scenarios.
Published in:	Proceedings of the 2024 International Technical Meeting of The Institute of Navigation January 23 - 25, 2024 Hyatt Regency Long Beach Long Beach, California
Pages:	1184 - 1191
Cite this article:	He, Yingzhe, Sun, Chao, Bai, Lu, Feng, Wenquan, Zhang, Shuai, Shah, Syed Shahid, "A 5G Adaptive Positioning Method in Environments with Significant Noise Changes," Proceedings of the 2024 International Technical Meeting of The Institute of Navigation, Long Beach, California, January 2024, pp. 1184-1191. https://doi.org/10.33012/2024.19571
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