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Improved Strong Tracking Cubature Kalman Filter for UWB Positioning.
Yuxiang Pu1, Xiaolong Li1,2, Yunqing Liu1,2
1Institute of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Sensors (Basel, Switzerland)
|September 9, 2023
Summary
This study introduces an improved strong tracking cubature Kalman filter (ISTCKF) for wireless ultra-wideband (UWB) positioning systems. The ISTCKF algorithm significantly reduces positioning errors caused by Non-Line-of-Sight (NLOS) issues and inaccurate models.
Area of Science:
- Engineering
- Computer Science
- Signal Processing
Background:
- Wireless ultra-wideband (UWB) positioning systems face challenges with Non-Line-of-Sight (NLOS) observation errors.
- Inaccurate predictive dynamics models further degrade the precision of UWB positioning.
Purpose of the Study:
- To propose an improved strong tracking cubature Kalman filter (ISTCKF) positioning algorithm.
- To address NLOS errors and improve the accuracy of UWB positioning systems.
Main Methods:
- Reconstructing observations using weighted results from predictive dynamics and least squares.
- Identifying NLOS observations by analyzing statistical differences between original and reconstructed data.
- Calculating fading factors based on NLOS identification to mitigate positioning errors.
Main Results:
- The ISTCKF algorithm effectively mitigates observation noise and main positioning errors in UWB systems.
- Experimental results show significant reductions in positioning error: 55.2% vs. STCKF, 32.3% vs. ACKF, and 28.9% vs. RSTCKF.
- The proposed ISTCKF algorithm demonstrates substantial improvements in positioning accuracy and system stability.
Conclusions:
- The ISTCKF algorithm offers a robust solution for enhancing UWB positioning accuracy.
- This method effectively handles NLOS errors and improves the reliability of UWB positioning systems.
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