Research on Fusion Localization Algorithm of Indoor UWB and IMU Assisted by GPR Error Calibration

MA Xinpeng; CHEN Yu; CUI Zhicheng; LI Xingguang; CUI Wei

doi:10.11999/JEIT241145

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MA Xinpeng, CHEN Yu, CUI Zhicheng, LI Xingguang, CUI Wei. Research on Fusion Localization Algorithm of Indoor UWB and IMU Assisted by GPR Error Calibration[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT241145

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MA Xinpeng, CHEN Yu, CUI Zhicheng, LI Xingguang, CUI Wei. Research on Fusion Localization Algorithm of Indoor UWB and IMU Assisted by GPR Error Calibration[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT241145

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Research on Fusion Localization Algorithm of Indoor UWB and IMU Assisted by GPR Error Calibration

doi: 10.11999/JEIT241145 cstr: 32379.14.JEIT241145

Changchun University of Science and Technology, School of Electronic information Engineering, Changchun 130022, China

Funds: Jilin Province Science and Technology Development Plan Project (20220203066SF)

Received Date: 2024-12-30
Rev Recd Date: 2025-07-28

Available Online: 2025-08-05

Abstract

Abstract

Objective Ultra-WideBand (UWB) ranging in confined indoor environments is prone to coarse ranging errors and Gaussian noise, which substantially degrade localization accuracy for both static and dynamic targets, even under Line-of-Sight (LOS) conditions. In addition, during indoor operations of wheeled vehicles, obstacles often obstruct the LOS between onboard UWB tags and anchors, resulting in Non-Line-of-Sight (NLOS) propagation. NLOS-induced interference results in abrupt fluctuations in range measurements, which severely compromise the estimation of the vehicle’s motion state. Methods To address these challenges, this study proposes a Gaussian Process Regression (GPR)-calibrated indoor UWB/Inertial Measurement Unit (IMU) fusion localization algorithm (GIU-EKF). The proposed approach offers two key features. First, a UWB ranging error model for the entire two-dimensional localization area is established by collecting UWB measurements at a limited set of reference points under LOS conditions. This model is applied to correct LOS-related ranging biases. Second, by leveraging the low short-term drift characteristics of inertial measurements, the algorithm fuses IMU and UWB data to mitigate NLOS ranging errors. Results and Discussions Under LOS conditions, a GPR-based error calibration model is constructed by sampling and analyzing UWB ranging deviations at known reference coordinates. This model captures the statistical association between two-dimensional spatial positions and the corresponding ranging errors. For any queried location, the model generates a set of probabilistic range estimates, with the final range value obtained by weighting nearby sample points according to their normalized likelihoods. This enables real-time suppression of LOS-related ranging errors. A threshold-based detection mechanism identifies NLOS conditions when the UWB range increment exceeds a predefined threshold. In NLOS scenarios, a subordinate Extended Kalman Filter (EKF) fuses UWB range data with short-term IMU measurements to compensate for NLOS-induced ranging errors during motion. The corrected range data are then incorporated into a primary EKF to update the vehicle’s motion state estimation. Experimental results demonstrate that the proposed GPR-based coarse error correction reduces localization errors by 64% and 58% for static and dynamic tags under LOS conditions, respectively. In three representative NLOS scenarios, the GIU-EKF algorithm maintains reliable motion state estimation for low-speed targets, achieving an average localization error of 7.5 cm. For tags moving at speeds between 0.2 m/s and 0.8 m/s, localization errors remain below 10 cm. The robustness of the proposed algorithm under extreme conditions is also validated. As shown in Section 4.3.2, the algorithm maintains stable velocity vector estimation even when the wheeled vehicle experiences alternating occlusions between single-anchor and multi-anchor configurations. Under low-speed conditions (2.2 cm/s), the localization error remains as low as 6.7 cm. Section 4.3.3 further verifies the algorithm’s convergence capability under large initial deviations. When subjected to initial heading errors between 5° and 50°, or a combined 1.5-meter position offset and 10° heading deviation, the proposed method consistently converges to the true position within a 2-meter travel distance. Conclusions This study presents a GPR-assisted indoor UWB/IMU fusion localization algorithm. By independently suppressing LOS and NLOS ranging errors from four UWB anchors, the proposed approach enhances localization accuracy in complex indoor environments. Under LOS conditions, a GPR-based correction mitigates coarse UWB ranging errors. In NLOS scenarios, short-term inertial estimates compensate for anomalous UWB measurements. A subordinate EKF adaptively balances observation uncertainties from the two sensing modalities, maintaining motion state observability when the tag moves slowly in confined spaces. This design avoids long-term drift accumulation, which is often observed in tightly coupled systems that treat IMU data as a strong prior, particularly when using low-cost inertial sensors. Experimental results demonstrate that the proposed algorithm achieves sub-10 cm localization accuracy under both LOS and NLOS conditions. During low-speed operations, the system maintains convergence of both velocity and position estimates. Furthermore, even with significant initial motion state biases, the algorithm consistently converges to the true trajectory. These findings indicate that the proposed method effectively meets the operational requirements of mobile robots in narrow indoor environments. However, practical application still requires further attention to two key aspects: efficient and autonomous collection of coordinate samples for GPR model training, and integration of real-time localization outputs with vehicle path planning and motion control systems.

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References(18)

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