A Seamless Pedestrian Localization System Based on GNSS/IMU/UWB/MAP Integration

Pedestrian localization has broad applications. However, achieving seamless positioning in complex environments remains a significant challenge due to factors like environmental occlusion and indoor-outdoor transitions. This article proposes a combined localization framework using global navigation satellite system (GNSS), inertial measurement unit (IMU), ultrawideband (UWB), and MAP data to address this issue. In the framework, a three-step GNSS localization method is proposed to enhance accuracy and stability by eliminating nonline-of-sight (NLOS) effects and filtering anomalies. Inertial navigation system (INS) and pedestrian dead reckoning (PDR) models are integrated to improve local precision and stability. UWB, known for its superior localization capabilities, has been integrated into some smartphones and is promising for large-scale indoor applications. This article presents a gradient descent-based optimal anchor deployment algorithm and localization methods for varying anchor quantities. Additionally, a nonstationary Markov model fuses map data for trajectory prediction and correction. Finally, anomaly detection filtering and low-pass filtering are applied to the corrected trajectories, yielding high accuracy and stability. Experiments on the Honor V20 and iQOO U3 smartphones showed significant improvements compared to the baseline algorithm. Outdoor root mean square error (RMSE) was reduced by 44.4% (10.83 m) for the Honor V20 and 40.0% (10.77 m) for the iQOO U3. Additionally, outdoor standard deviation (STD) improved by 43.9% (4.80 m) and 43.5% (5.50 m), respectively. In transitional areas, RMSE improved by 4.61% (10.77 m) and 19.6% (4.76 m), respectively. Indoor RMSE also showed improvements, with the Honor V20 reducing it by 8.00% (3.79 m) and the iQOO U3 by 5.78% (3.91 m).