An Indoor Fingerprint Positioning Algorithm Based on WKNN and Improved XGBoost

Considering the low indoor positioning accuracy and poor positioning stability of traditional machine-learning algorithms, an indoor-fingerprint-positioning algorithm based on weighted k-nearest neighbors (WKNN) and extreme gradient boosting (XGBoost) was proposed in this study. Firstly, the outlier...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-04, Vol.23 (8), p.3952
Main Authors: Lu, Haizhao, Zhang, Lieping, Chen, Hongyuan, Zhang, Shenglan, Wang, Shoufeng, Peng, Huihao, Zou, Jianchu
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Data mining
Decision trees
Distribution functions
Fingerprints
genetic algorithm
Genetic algorithms
Global positioning systems
GPS
indoor localization
Localization
Machine learning
Outliers (statistics)
Positioning devices (machinery)
Radio frequency identification
Signal processing
Signal strength
Statistical analysis
Statistical methods
WiFi fingerprint
Wireless access points
WKNN
XGBoost
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Summary:Considering the low indoor positioning accuracy and poor positioning stability of traditional machine-learning algorithms, an indoor-fingerprint-positioning algorithm based on weighted k-nearest neighbors (WKNN) and extreme gradient boosting (XGBoost) was proposed in this study. Firstly, the outliers in the dataset of established fingerprints were removed by Gaussian filtering to enhance the data reliability. Secondly, the sample set was divided into a training set and a test set, followed by modeling using the XGBoost algorithm with the received signal strength data at each access point (AP) in the training set as the feature, and the coordinates as the label. Meanwhile, such parameters as the learning rate in the XGBoost algorithm were dynamically adjusted via the genetic algorithm (GA), and the optimal value was searched based on a fitness function. Then, the nearest neighbor set searched by the WKNN algorithm was introduced into the XGBoost model, and the final predicted coordinates were acquired after weighted fusion. As indicated in the experimental results, the average positioning error of the proposed algorithm is 1.22 m, which is 20.26-45.58% lower than that of traditional indoor positioning algorithms. In addition, the cumulative distribution function (CDF) curve can converge faster, reflecting better positioning performance.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23083952