A Mixed Optimization Method Based on Adaptive Kalman Filter and Wavelet Neural Network for INS/GPS During GPS Outages

Xiaokai Wei,Pengyun Li,Kaiqiang Feng,Yubing Jiao,Lening Zhao,Jie Li,Debiao Zhang

doi:10.1109/access.2021.3068744

Xiaokai Wei, Pengyun Li + Show 5 more

Open Access

https://doi.org/10.1109/access.2021.3068744

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Abstract

To improve the navigation performance of the navigation system combining inertial navigation system (INS) and global positioning system (GPS) under complicated environments, especially GPS outages, a navigation method - wavelet neural network based on random forest regression (RFR-WNN) to assist adaptive Kalman filter (AKF) - is proposed. AKF is employed to correct INS errors, the Kalman filter is improved by introducing adaptive factor, to suppress the influence of the complex environment and random errors on the filtering accuracy; RFR-WNN is used to construct a high-precision prediction model when GPS works well, and to provide the required observations for AKF update when GPS outages. To solve the problem that the single neural network structure is easy to cause the overfitting, unstable and low prediction accuracy due to the lack of comprehensive training samples, RFR is introduced to optimize the single WNN, which can improve the generalization ability and prediction accuracy. In order to verify the effectiveness and advancement of the proposed method, vehicle navigation experiments were carried out, the results indicate that the proposed method has better navigation accuracy and performance than compared methods during GPS outages, and this advantage is more obvious in the case that fewer samples are collected.

Highlights

Navigation technology is widely used in military and civil engineering fields, how to provide a reliable and high-precision navigation method is the key to realize navigation applications [1], [2]
According to the overall structure of the proposed random forest regression (RFR)-wavelet neural network (WNN)+adaptive Kalman filter (AKF), measured values of accelerometer and gyroscope are selected as the input, the corresponding position and velocity differences between the global positioning system (GPS) and the inertial navigation system (INS) are collected as the output for the training mode when GPS is available, and observations required for AKF is obtained continuously from the RFR-WNN of INS/GPS integrated system when GPS signal is defective
In this paper, we proposed a novel method that is WNN based on RFR to assist AKF, to provide a reliable and high performance navigation solution for INS/GPS integrated navigation system during GPS outages

Summary

INTRODUCTION

Navigation technology is widely used in military and civil engineering fields, how to provide a reliable and high-precision navigation method is the key to realize navigation applications [1], [2]. Xu et al [20] proposed a land-based vehicle positioning scheme in which the grey system model is mapped to the back-propagation NN and the grey NN is used to assist the UKF, which can bridge GPS outages simultaneously; Zhang [21] combined empirical modal decomposition threshold filtering and long short-term memory NN to provide GPS pseudo-range, so as to bridge GPS outages; Yao et al [22] combined KF and improved multi-layer perceptron network, the pseudo-distance of GPS is predicted and estimated when GPS is defective; Chen and Fang [23] used radial basis function NN combined with time series analysis predict the KF measurement update for bridging GPS outages; Abdolkarimi et al [24] optimized a new NN based on extreme learning machine for predicting and correcting INS errors when GPS signal is interrupted; Aggarwal et al [25] combined D-S evidence theory with NN to reduce the complexity of neural network learning, improving the efficiency of learning and the effectiveness of prediction; Sharaf and Noureldin [26] proposed a real-time data fusion technology based on radial basis function NN for GPS and inertial sensors integration, which enhanced the real-time prediction; Jwo et al [27] introduced NN into the vector tracking loop to improve the GPS positioning performance when GPS signal is blocked. RFR-WNN is trained online when GPS is available while RFR-WNN provides observation input for the AKF update process during GPS outages

OVERVIEW OF THE PROPOSED SOLUTION

PROPOSED RFR-WNN METHOD

COMPARISON RESULTS BETWEEN DIFFERENT ALGORITHMS

Findings

CONCLUSION