A Robust EMD-Based RVFL Network Fusion Algorithm for Low-Cost GPS/INS Integrated System

Da Liu,Shufang Zhang,Jingbo Zhang

doi:10.1155/2019/9653237

Da Liu, Shufang Zhang + Show 1 more

Open Access

https://doi.org/10.1155/2019/9653237

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Abstract

Global positioning system (GPS) and inertial navigation system (INS) are commonly combined to overcome disadvantages of each and constitute an integrated system that realizes long-term precision. However, the performance of the integrated system deteriorates on which GPS is unavailable. Especially when low-cost inertial sensors based on the microelectromechanical system (MEMS) are used, performance of the integrated system degrades severely over time. In this study, in order to minimize the adverse impact of high-level stochastic noise from low-cost MEMS sensors, denoising technology based on empirical mode decomposition (EMD) is employed to improve signal quality before navigation solution by which significant improvement of removing noise is achieved. Moreover, a random vector functional link (RVFL) network-based fusion algorithm is presented to estimate and compensate position error during GPS outage such that error accumulation is suppressed quickly when INS is working standalone. Performance of the proposed approach is evaluated by experimental results. It is indicated from comparison that the proposed algorithm takes advantages such as better accuracy and lower complexity and is more robust than the commonly reported methods and is more appropriate for real-time and low-cost application.

Highlights

Nowadays, navigation technology has attracted more attention than ever before on account of the increasing demand for positioning or location in various fields such as consumer electronics, displacement monitoring, and intelligent transportation
To acquire superior performance of the Global positioning system (GPS)/ inertial navigation system (INS) integrating system with low-cost inertial sensors in the absence of GPS signal, we focus on the advantages of random vector functional link (RVFL) and investigate this promising network and evaluate its effectiveness in integrated navigation field
To evaluate and verify performance of the proposed integrated navigation system, field experiment is conducted in Dalian, Liaoning Province, China. e duration of the experiment is 2400 s, and the driving trajectory of the vehicle with navigation equipment is shown with colored line in Figure 4. e marked red line denotes simulated GPS outage in which different driving dynamics are considered

Summary

Introduction

Navigation technology has attracted more attention than ever before on account of the increasing demand for positioning or location in various fields such as consumer electronics, displacement monitoring, and intelligent transportation. Wavelet analysis is a commonly efficient tool for signal denoising, which can be facilely and effectively implemented in the integrated navigation system to eliminate high-frequency noise via multiresolution features and improve system accuracy [14] In this background, Abdolkarimi et al proposed a wavelet-based ELM model to predict INS errors during GPS outage [15]. Motivated by achieving and enhancing continuous highprecision operation performance even during the GPS outages, a novel AI-based methodology for the low-cost INS/GPS navigation system is proposed in this paper to solve the problems existing in the methods mentioned above, in which the high-frequency noise from low-cost sensors are suppressed by EMD denoising technology and high positioning accuracy, and real-time learning capability are obtained by taking advantage of the fast learning of RVFL.

EMD-Based Denoising

Integrated System Based on RVFL

Experimental Results and Discussion

Conclusions