Loosely Coupled GNSS/INS Integration Based on Factor Graph and Aided by ARIMA Model

The integration of global navigation satellite system (GNSS) and inertial navigation system (INS) has been widely studied in the past decades. The traditional Kalman filtering method, a procedure that leads to historical states and observations loss, can estimate fast by limiting the update to the l...

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Bibliographic Details
Published in:IEEE sensors journal 2021-11, Vol.21 (21), p.24379-24387
Main Authors: Li, Qiumei, Zhang, Lingwen, Wang, Xiaolin
Format: Article
Language:English
Subjects:
Algorithms
ARIMA
autonomous driving
Autoregressive models
data gap
Data models
Factor graph
Global navigation satellite system
GNSS
Inertial navigation
INS
Kalman filters
Navigation
Navigation systems
Obstructions
Prediction algorithms
Predictive models
Sensors
State estimation
Time measurement
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Summary:The integration of global navigation satellite system (GNSS) and inertial navigation system (INS) has been widely studied in the past decades. The traditional Kalman filtering method, a procedure that leads to historical states and observations loss, can estimate fast by limiting the update to the latest state. Furthermore, the performance of loosely coupled GNSS/INS navigation integration depends largely on environmental conditions and sensor costs. In cities, GNSS signals may be interrupted due to the influence of obstructions and moving objects, which leads to the discontinuity of GNSS/INS navigation. Therefore, in order to overcome these difficulties an auto regressive integrated moving average (ARIMA) auxiliary model based on time sequence is proposed in this paper, which makes use of the data before interruption to predict the GNSS measurements during the interruption period and makes up for the data gap. In addition, the INS and GNSS measurements are loosely coupled using the most advanced factor graph model. Compared to the filtering algorithm, all previous measurements are used for state estimation through the factor graph framework. In this paper, the experiment is based on the dataset of Tokyo which is a typical challenging city canyon. Experiments showed that, compared with the traditional GNSS/INS integration method, the proposed method can provide a higher precision and a series of continuous navigation results even when the GNSS measurement data was interrupted.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2021.3112490