UWB Localization Based on Improved Robust Adaptive Cubature Kalman Filter

Aiming at the problems of Non-Line-of-Sight (NLOS) observation errors and inaccurate kinematic model in ultra-wideband (UWB) systems, this paper proposed an improved robust adaptive cubature Kalman filter (IRACKF). Robust and adaptive filtering can weaken the influence of observed outliers and kinem...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-02, Vol.23 (5), p.2669
Main Authors: Dong, Jiaqi, Lian, Zengzeng, Xu, Jingcheng, Yue, Zhe
Format: Article
Language:English
Subjects:
Accuracy
adaptive filtering
Adaptive filters
Algorithms
Kalman filtering
Kalman filters
Kinematics
Localization
Location-based systems
Methods
NLOS signal
Outliers (statistics)
Parameter estimation
Polynomials
Position errors
Propagation
Receivers & amplifiers
robust filtering
Ultra wideband technology
ultra-wideband
Ultrawideband
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Summary:Aiming at the problems of Non-Line-of-Sight (NLOS) observation errors and inaccurate kinematic model in ultra-wideband (UWB) systems, this paper proposed an improved robust adaptive cubature Kalman filter (IRACKF). Robust and adaptive filtering can weaken the influence of observed outliers and kinematic model errors on filtering, respectively. However, their application conditions are different, and improper use may reduce positioning accuracy. Therefore, this paper designed a sliding window recognition scheme based on polynomial fitting, which can process the observation data in real-time to identify error types. Simulation and experimental results indicate that compared to the robust CKF, adaptive CKF, and robust adaptive CKF, the IRACKF algorithm reduces the position error by 38.0%, 45.1%, and 25.3%, respectively. The proposed IRACKF algorithm significantly improves the positioning accuracy and stability of the UWB system.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23052669