Citation: Tang, Y.; Jiang, J.; Liu, J.;
Yan, P.; Tao, Y.; Liu, J. A GRU and
AKF-Based Hybrid Algorithm for
Improving INS/GNSS Navigation
Accuracy during GNSS Outage.
Remote Sens. 2022, 14, 752. https://
doi.org/10.3390/rs14030752
Academic Editors: Kamil Krasuski
and Damian Wierzbicki
Received: 10 January 2022
Accepted: 29 January 2022
Published: 6 February 2022
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Article
A GRU and AKF-Based Hybrid Algorithm for Improving
INS/GNSS Navigation Accuracy during GNSS Outage
Yanan Tang
1
, Jinguang Jiang
1,
* , Jianghua Liu
2
, Peihui Yan
1
, Yifeng Tao
1
and Jingnan Liu
1
1
GNSS Research Center, Wuhan University, Wuhan 430079, China; lucytang@whu.edu.cn (Y.T.);
phuiyan@whu.edu.cn (P.Y.); yifengtao@whu.edu.cn (Y.T.); jnliu@whu.edu.cn (J.L.)
2
School of Electronics and Information Engineering, Hubei University of Science and Technology,
Xianning 437100, China; jianghualiu@hbust.edu.cn
* Correspondence: jinguang@whu.edu.cn
Abstract:
The integrated navigation system consisting of an inertial navigation system (INS) and
Global Navigation Satellite System (GNSS) provides continuous high-accuracy positioning whereas
the navigation accuracy during a GNSS outage inevitably degrades owing to INS error divergence.
To reduce such degradation, a gated recurrent unit (GRU) and adaptive Kalman filter (AKF)-based
hybrid algorithm is proposed. The GRU network, which has advantages of high accuracy and
efficiency, is constructed to predict the position variations during GNSS outage. Furthermore,
this paper takes the GRU-predicted error accumulation into consideration, and introduces AKF as a
supplementary methodology to improve the navigation performance. The proposed hybrid algorithm
is trained and tested by practical road datasets and compared with four algorithms, including the
standard KF, Multi-Layer Perceptron (MLP)-aided KF, Long Short Time Memory (LSTM) aided KF,
and GRU-aided KF. Periods of 180 and 120 s GNSS outage are employed to test the performance of
the proposed algorithm in different time scales. The comparison result between the standard KF and
neural network-aided KF indicates that the neural network is an effective methodology for bridging
GNSS outages. The performance comparison between three kinds of neural networks demonstrate
that both recurrent neural networks surpass the MLP in prediction position variation, and the GRU
transcends the LSTM in prediction accuracy and training efficiency. Furthermore, it is concluded that
the adaptive estimation theory is an effective complement to neural network-aided navigation, as the
GRU-aided AKF reduced the horizontal error of GRU-aided KF by 31.71% and 16.12% after 180 and
120 s of GNSS outage, respectively.
Keywords:
INS/GNSS integrated navigation; GNSS outage; GRU neural network; AKF;
innovation-based
adaptive estimation
1. Introduction
INS and GNSS are two of the most widely used navigation techniques in both civilian
and military fields. GNSS provides high accuracy position and velocity information with
a relatively stable noise level in open-sky outdoor environments [
1
,
2
]. Nevertheless, it
suffers from the shortcoming of signal vulnerability, which leads to accuracy degradation
in complex urban environment, including overpasses, boulevards, and urban canyons, etc.
On the other hand, INS is a self-contained navigation system that estimates the position,
velocity, and attitude with a high update frequency. Although, INS suffers from a drawback
in that its error accumulates over time [
3
]. Since INS and GNSS have complementary
characteristics, they are combined as an INS/GNSS integrated navigation system which
surpasses both stand-alone systems [4–7].
Although INS error could be estimated and compensated for by the KF-based INS/GNSS
integration algorithm [
8
], low-cost INS error accumulates rapidly [
9
] during GNSS outage.
Generally, there are, in general, two categories of techniques used to enhance low-cost INS
Remote Sens. 2022, 14, 752. https://doi.org/10.3390/rs14030752 https://www.mdpi.com/journal/remotesensing
