INS Error Estimation Based on an ANFIS and Its Application in Complex and Covert Surroundings

Yabo Duan,Suqin Wu,Kefei Zhang,Huaizhan Li

doi:10.3390/ijgi10060388

Abstract

Inertial navigation is a crucial part of vehicle navigation systems in complex and covert surroundings. To address the low accuracy of vehicle inertial navigation in multifaced and covert surroundings, in this study, we proposed an inertial navigation error estimation based on an adaptive neuro fuzzy inference system (ANFIS) which can quickly and accurately output the position error of a vehicle end-to-end. The new system was tested using both single-sequence and multi-sequence data collected from a vehicle by the KITTI dataset. The results were compared with an inertial navigation system (INS) position solution method, artificial neural networks (ANNs) method, and a long short-term memory (LSTM) method. Test results indicated that the accumulative position errors in single sequence and multi-sequences experiments decreased from 9.83% and 4.14% to 0.45% and 0.61% by using ANFIS, respectively, which were significantly less than those of the other three approaches. This result suggests that the ANFIS can considerably improve the positioning accuracy of inertial navigation, which has significance for vehicle inertial navigation in complex and covert surroundings.

Highlights

The commonly used solutions are the utilizing of artificial neural networks [7], delayed neural networks [8], adaptive neural fuzzy inference system (ANFIS) [9,10], and other neural networks [11]
Experiments and Results predicted by the artificial neural networks (ANNs) model, and the position error predicted by the ANFIS were comPublic datasets are utilized for is training and predicting one sequence in different pared
Error predicted by the ANN model, and the position error predicted by the ANFIS were compared

Summary

Introduction

When the GPS signal is blocked for a long time without the aid of other sensors, the error of the position obtained by an INS is most likely to be large, which is the major problem to be solved in this study. In this case, a machine learning method can be used to compensate for the error. Using the IMU data as the observations theroads, GPS measurements sensors video cameras, laser scanners, GPS, and INS on and urban country roads, as the reference, the frequency of both observations in the unrectified datawere is 100used Hz. and expressways [52,53]. Using the IMU data as the observations and the GPS measurements as the the dataformat.txt file contains the GPS and in data,raw while the reference, the frequency of both observations theformat unrectified data is timestamps.txt

Methods

Results

Discussion

Conclusion