UWB Wireless Positioning Method Based on LightGBM

• Published in: Wireless personal communications Vol. 137; no. 2; pp. 1141 - 1160
• Main Authors: Cui, Xuerong, Li, Yuanxu, Li, Juan, Jiang, Bin, Li, Shibao, Liu, Jianhang
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Communications Engineering; Computer Communication Networks; Correlation coefficients; Engineering; Error analysis; Error correction; Genetic algorithms; Impulse response; Networks; Positioning devices (machinery); Regression models; Signal,Image and Speech Processing; Ultrawideband; UWB; CNN; NLOS identification; LightGBM; Indoor positioning; Machine Learning; Error mitigation
• ISSN: 0929-6212; 1572-834X
• DOI: 10.1007/s11277-024-11456-x

In the ultra-wideband indoor positioning sceneraio, the non-line of sight (NLOS) propagation may be caused by obstacles, which may lead to the deviation of ranging value and affect the positioning precision. Therefore, we propose a NLOS identification and error regression positioning algorithm based on light gradient boosting machine (LightGBM). Firstly, ReliefF algorithm combined with Spearman correlation coefficient is used to analyze the feature correlation, and eight channel features such as total channel impulse response power and standard deviation of noise are selected as NLOS identification features. Then, we adopt genetic algorithm to optimize the hyperparameters of LightGBM for NLOS identification. On this basis, the proposed error regression model based on convolutional neural network (CNN) combined with LightGBM is used to correct the ranging results, so as to achieve high-precision positioning. Through the verification on the public dataset, the NLOS identification accuracy reached 91.8%, and the positioning precision is improved by 45 cm after correcting the ranging results.