A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS during GNSS Signal Outages

Wei Fang,Peihui Yan,Haiyong Luo,Yanan Tang,Yilin Gong,Yifeng Tao,Jinguang Jiang,Shuangqiu Lu,Jingnan Liu

doi:10.3390/rs12020256

Abstract

Aiming to improve the navigation accuracy during global navigation satellite system (GNSS) outages, an algorithm based on long short-term memory (LSTM) is proposed for aiding inertial navigation system (INS). The LSTM algorithm is investigated to generate the pseudo GNSS position increment substituting the GNSS signal. Almost all existing INS aiding algorithms, like the multilayer perceptron neural network (MLP), are based on modeling INS errors and INS outputs ignoring the dependence of the past vehicle dynamic information resulting in poor navigation accuracy. Whereas LSTM is a kind of dynamic neural network constructing a relationship among the present and past information. Therefore, the LSTM algorithm is adopted to attain a more stable and reliable navigation solution during a period of GNSS outages. A set of actual vehicle data was used to verify the navigation accuracy of the proposed algorithm. During 180 s GNSS outages, the test results represent that the LSTM algorithm can enhance the navigation accuracy 95% compared with pure INS algorithm, and 50% of the MLP algorithm.

Highlights

The inertial navigation system (INS) and global navigation satellite system (GNSS) are two of the main and most important approaches for providing position and attitude information for geographical references [1]
For all of the above reasons, this study suggests that a long short-term memory (LSTM) neural network identifying a nonlinear dynamic process can solve the above drawbacks of those models, which have capabilities to perform highly nonlinear dynamic mapping and store past information [23]
The artificial intelligence (AI) model is comprised of two LSTM layer and a fully-connected layer activated by softmax function

Summary

Introduction

The inertial navigation system (INS) and global navigation satellite system (GNSS) are two of the main and most important approaches for providing position and attitude information for geographical references [1]. GNSS alone cannot give reliable positions all of the time, as the satellite signal may be blocked or corrupted as a result of high buildings, viaducts, tunnels, mountains, multi-path reflections, and bad weather conditions [3,4,5]. Because of their complementary properties, INS and GNSS are commonly integrated by a Kalman filter (KF) for providing continuous and high precision navigation [3,4,5,6]. An improved fusion algorithm needs to be explored so as to improve the INS navigation performance when the GNSS signal is lost

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