Indoor Positioning Algorithm for RFID Based on Newton Interpolation and Support Vector Regression Optimized by Hybridizing Grey Wolf Optimization

doi:10.16182/j.issn1004731x.joss.201709007

Abstract

Abstract: Since the traditional LANDMARC location algorithms have poor positioning accuracy and cost laborious efforts to decorate reference tags in the RFID positioning system, a novel positioning algorithm proposed is based on the Newton interpolation method and support vector regression optimized by hybridizing grey wolf optimization with differential evolution (HGWOSVR). By using the proposed algorithm, Gaussian filter was used to deal with the sampling data of the reference tags.Newton interpolation method was adopted to estimate the RSS value of other reference tags to expand database. The HGWOSVR algorithm was employed to build the nonlinear relationship between the RSS value of reference tags and their locations to predict the positioned tags. Simulation results show that the proposed algorithm performs better in terms of positioning accuracy, and reduces the workload of decorating reference tags, which improves working efficiency of the indoor location positioning method.

Key words: RFID, Newton interpolation, DE algorithm, GWO algorithm, SVR algorithm

CLC Number:

TP391.9

Xu Yangjie, Wang Yan, Yan Dahu, Ji Zhicheng. Indoor Positioning Algorithm for RFID Based on Newton Interpolation and Support Vector Regression Optimized by Hybridizing Grey Wolf Optimization[J]. Journal of System Simulation, 2017, 29(9): 1921-1929.

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