Machine Learning-Based Ranging Correction Using CIR in UWB 


Vol. 51,  No. 3, pp. 574-585, Mar.  2026
10.7840/kics.2026.51.3.574


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  Abstract

Ultra-Wideband (UWB) enables high-precision distance measurements owing to a bandwidth over 500 MHz. Its pulse-like signals can be converted into Channel Impulse Response (CIR), revealing detailed channel characteristics. However, real-world conditions such as Non-Line-of-Sight (NLOS) path, multipaths, and hardware noises degrade measurement accuracy. To address these challenges, we propose a machine learning-based ranging correction method using CIR-based features. We conducted experiments with four UWB anchors and 24 ground-truth points in an indoor parking lot. Localization error was calculated as the Euclidean distance between estimated and actual positions. Seven CIR-based features were extracted from the measured channel impulse responses, such as Root Mean Square delay spread. These features were used as the input for three regression models: Random Forest, Gradient Boosting Regression, and Support Vector Regression. An exhaustive feature selection identified four optimal inputs, reducing average localization error from 0.227 m (without ranging correction) to 0.127 m for the proposed method, which is a 44.1% improvement. To investigate the regression model performance, each feature’ s capability to distinguish Line-of-Sight (LOS) and NLOS was quantified using Effect Size and compared with Permutation Feature Importance. A strong correlation (r = 0.863) was observed, confirming the high LOS/NLOS separability features were more important in model learning.

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[IEEE Style]

C. Kim, D. Ham, S. Kim, "Machine Learning-Based Ranging Correction Using CIR in UWB," The Journal of Korean Institute of Communications and Information Sciences, vol. 51, no. 3, pp. 574-585, 2026. DOI: 10.7840/kics.2026.51.3.574.

[ACM Style]

Check Kim, Doyoung Ham, and Seong-Cheol Kim. 2026. Machine Learning-Based Ranging Correction Using CIR in UWB. The Journal of Korean Institute of Communications and Information Sciences, 51, 3, (2026), 574-585. DOI: 10.7840/kics.2026.51.3.574.

[KICS Style]

Check Kim, Doyoung Ham, Seong-Cheol Kim, "Machine Learning-Based Ranging Correction Using CIR in UWB," The Journal of Korean Institute of Communications and Information Sciences, vol. 51, no. 3, pp. 574-585, 3. 2026. (https://doi.org/10.7840/kics.2026.51.3.574)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880