Indoor Positioning Algorithm Based on XGBoost Prediction and Elastic Net Error Compensation

doi:10.16182/j.issn1004731x.joss.20-0866

Abstract

Abstract:

Arming at the decreased positioning accuracy caused by the environment dynamic change of indoor positioning system, an error compensation algorithm based on XGBoost fusion elastic net is proposed. XGBoost positioning model is used to make a preliminary prediction on the target position. When the indoor environment changes, the elastic net algorithm is used to construct an error compensation model to correct the positioning error of XGBoost positioning model. The experimental results show that when only 15% of the fingerprint database samples need to be updated, the positioning accuracy of the proposed algorithm is controlled in 0.73m at the 80% percentile, which is significantly better than those of the K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF) and gradient boosting decision tree (GBDT) positioning algorithms, and the accuracy increases 25.5% than XGBoost.

Key words: indoor positioning, WiFi fingerprint, XGBoost, elastic net, error compensation

CLC Number:

TP391

Xiaofei Kang, Xuan Zeng, Wei Qiao. Indoor Positioning Algorithm Based on XGBoost Prediction and Elastic Net Error Compensation[J]. Journal of System Simulation, 2022, 34(4): 719-726.

Figures/Tables 7

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算法	平均定位精度/m	80%分位点精度/m	时间复杂度
KNN	2.02	2.75	O (n·m)
SVM	1.21	1.33	O (n²·m)
RF	1.02	1.48	O (n·log n·m·k)
GBDT	0.87	1.01	O (n·log n·m·d)
XGBoost	0.71	0.98	O (p·log n+k·d·p)
XGBoost-EC	0.47	0.73	O (p·log n+k·d·p)

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