Ultrawideband Ranging Data Reconstruction and Positioning Calculation Framework in Indoor Low-Dynamic Scenarios

Ultrawideband (UWB) has received widespread attention from many scholars in recent years owing to its high positioning precision. At present, there are problems in UWB indoor positioning, such as incomplete ranging data processing algorithms and difficulty in achieving parameter estimation without prior initial values. According to these problems, this paper constructs a positioning calculation framework in an indoor low-dynamic scenario, which is based on the reconstruction of ultra-wideband ranging data. Firstly, a novel UWB ranging data outliers detection algorithm based on gradient guidance is proposed. Secondly, a UWB ranging data reconstruction algorithm, employing modified Akima piecewise cubic Hermite interpolation, is proposed to reconstruct ranging observations systematically. Finally, a positioning calculation framework combining the Chan algorithm and the Extended Kalman Filter (EKF) is established. Several experiments results validate the effectiveness of the proposed methods. The outliers detection algorithm successfully eliminates ranging outliers in both static and dynamic positioning experiments, particularly showcasing notable improvements in dynamic test, with data completeness and effectiveness rates surpassing 95%. Integrating the EKF with the Chan algorithm significantly enhances the positioning algorithm's robustness, improving both continuity and smoothness in positioning results. Static positioning experiments reveal a substantial improvement, with a 98.9% decrease in standard deviation (STD) from 35.48 cm to 0.39 cm and a 98.4% decrease in root mean square error (RMSE) from 35.51 cm to 0.58 cm. The dynamic positioning experiment shows that the positioning trajectory obtained by the proposed method is more precise and smoother. The proposed method significantly elevates both the quality of raw observation data and the completeness and reliability of positioning results.