Enhancing seamless INS/UWB integrated localization using LS-SVM assisted predictive adaptive Kalman filter

Yuan Xu,Ning Feng,Maoyong Nie,Shuhui Bi,Tao Shen

doi:10.1109/iciai.2019.8850740

Enhancing seamless INS/UWB integrated localization using LS-SVM assisted predictive adaptive Kalman filter

Yuan Xu, Ning Feng + Show 3 more

https://doi.org/10.1109/iciai.2019.8850740

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Publication Date: Jul 1, 2019

Citations: 11

Affiliation: University of Jinan

#Predictive Kalman Filter #Least Squares-support Vector Machines + Show 8 more

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Abstract

This paper presents a least squares-support vector machines (LS-SVM) assisted predictive Kalman filter (PKF). In this model, the PKF can fuse the ultra wide band (UWB)- and inertial navigation system (INS)-based measurement, and compensate the missing data. Moreover, the LS-SVM is used to build the mapping between theoretical values and observation vectors, which can improve the accuracy. The experimental study indicates the effectiveness of the proposed LS-SVM assist PKF.

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