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A Modified Neuro-Fuzzy System for Accuracy Improvement of Low-Cost MEMS-Based INS/GPS Navigation System

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Abstract

Integration of an inertial navigation system (INS) and global positioning system (GPS) assures the reliability and accuracy of navigation solutions compared with standalone GPS or INS. In the present study, a low-cost micro-electro-mechanical system (MEMS)-grade INS/GPS navigation system is improved for predicting the INS sensor errors at GPS interruptions. Because of some conditions, including high stochastic noise of low-cost MEMS-grade inertial sensor, highly complex model of real noisy data, the non-linear dynamic environment, the high-speed vehicle, and the long-term absence of GPS navigation system during our experiments, a modified neuro-fuzzy system is proposed to handle the prediction of the INS positioning error during the long-term GPS interruptions. The proposed method using an iterative rule-based optimization technique provides both accuracy maximization and complexity minimization, with fewer rules than the adaptive neuro-fuzzy inference system (ANFIS). This method aims to enhance the calculation efficiency and improve the speed and accuracy of estimation in real-time applications. The effectiveness of the proposed integrated solution is assessed through real field test data in a flight scenario using a high-speed vehicle. The achieved results are also compared against ANFIS, wavelet neural network (WNN), extreme learning machine (ELM), and the extended Kalman filter (EKF) methods. Herein, the achieved results show considerable accuracy improvement in positioning compared to ANFIS, ELM, and the EKF for low-cost MEMS-grade INS sensors in the long-term GPS interruptions by approximately 41% and 79%, respectively.

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Data Availability

Our data have been gathered with a flight test aircraft flying along the trajectory in a pilot and hospitality training center named Meraj Aviation Academy.

Code Availability

We have coded our proposed algorithm in C in a Matlab environment.

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Funding

This research received no external funding.

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Authors and Affiliations

  1. Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, 16846-13114, Iran

    Elahe Sadat Abdolkarimi & Mohammad Reza Mosavi

Authors
  1. Elahe Sadat Abdolkarimi
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  2. Mohammad Reza Mosavi
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Contributions

Conceptualization, ESA and MRM; Investigation, ESA; Methodology, ESA; Supervision, MRM; Validations, ESA; Original draft writing, ESA; Review and editing, MRM. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Mohammad Reza Mosavi.

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Appendix

Appendix

Appendix 1 results of the EKF and ANFIS during 20-s GPS outage (1) in the x, y, and z directions (in ECEF coordinate). (See Figs.

Fig. 15
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The original position and the EKF output using the EKF method in meters in a the x-direction, b y-direction, and c z-direction

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Fig. 16
figure 16

The actual positioning error and the predicted position error of proposed method during 20-s GPS outage (1) using ANFIS method in a the x-direction, b y-direction, and c z-direction

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16)

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Abdolkarimi, E.S., Mosavi, M. A Modified Neuro-Fuzzy System for Accuracy Improvement of Low-Cost MEMS-Based INS/GPS Navigation System. Wireless Pers Commun 129, 1369–1392 (2023). https://doi.org/10.1007/s11277-023-10194-w

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