Application of Extreme Learning Machine in GPS Positioning Process

Chengxin Li,Jing Peng,Gang Ou,Mengli Wang,Lv Zhicheng,Changde Lu,Jianning Su,Xuesheng Pei

doi:10.1051/matecconf/201817601034

Abstract

In the positioning process of GPS, the linear least squares algorithm and Kalman filtering algorithm are widely used but still have shortcomings. Application of extreme learning machine in this area is proposed in this paper, which breaks through the limitations of the traditional method of positioning based on mathematical models. Two simulation experiments of ELM in GPS positioning process are presented in this paper while the latter is a supplement to the former. Each one contains three phases, including simulation data generation, network training and network prediction, each of which is considered carefully. The feasibility of extreme learning machine is verified through experimental simulation. A more accurate positioning result can be obtained.

Highlights

In the positioning process of the global positioning system (GPS), the linear least squares (LS) algorithm and Kalman filtering (KF) algorithm are usually used [1]
The KF algorithm depends on the statistical properties of the noise of the state model and the measurement model, which may lead to the divergence of the filter [2, 3]
Artificial neural network (ANN) is a nonlinear dynamic system composed of a large number of simple processing units interconnected by a certain structure [4]

Summary

Introduction

In the positioning process of the global positioning system (GPS), the linear least squares (LS) algorithm and Kalman filtering (KF) algorithm are usually used [1]. The KF algorithm depends on the statistical properties of the noise of the state model and the measurement model, which may lead to the divergence of the filter [2, 3]. Both algorithms have good performance practically, there are still some problems to solve. Artificial neural network (ANN) is a nonlinear dynamic system composed of a large number of simple processing units (neurons) interconnected by a certain structure [4]. It is the simplification and simulation of human brain information processing mechanism. The result of ELM is compared to that of least linear squares method and Kalman filtering method

Positioning observation equation

Extreme learning machine

Application of ELM in GPS positioning process

Conclusion