Improved indoor location algorithm based on Wi-Fi fingerprint and random forest
Konferenz: CIBDA 2022 - 3rd International Conference on Computer Information and Big Data Applications
25.03.2022 - 27.03.2022 in Wuhan, China
Tagungsband: CIBDA 2022
Seiten: 5Sprache: EnglischTyp: PDF
Autoren:
Han, Xuefa (School of Computer Science, Wuhan Donghu University, Wuhan, China)
Han, Xuefang (College of Education, Zhejiang University of Technology, Hangzhou, China)
Wu, Fei; Zhu, Hai (School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China)
Inhalt:
Aiming at the influence of the similarity of Wi-Fi signal strength on indoor positioning, this paper proposes an improved indoor positioning algorithm based on Wi-Fi fingerprint and random forest. The algorithm uses Wi-Fi as a signal source to construct a Wi-Fi fingerprint library by receiving signal strength indication and basic service set identifiers, thereby establishing a random forest model for indoor location sensing. The simulation results show that the positioning error of the algorithm is about 2.26m. Compared with similar algorithms, it has better performance in execution time and positioning accuracy, and the accuracy of the algorithm is improved by about 3.2%.