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GNSS/INS Tightly Coupled Navigation with Robust Adaptive Extended Kalman Filter

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Abstract

GNSS/INS integrated navigation system is particularly outstanding in providing reliable navigation information for land vehicle applications. However, GNSS measurements are easily disturbed in harsh operating environments, especially the accuracy of integrated navigation system integrated with inertial navigation system will be affected accordingly. Hence, a robust adaptive extended Kalman filter procedure is crucial to maintain the stability and reliability of the system. In this study, a robust factor based on local test of standardized residual vector was proposed to deal with potential gross errors, and an adaptive factor based on position dilution of precision which reflect the satellite geometry was proposed to adjust covariance matrix. The robust adaptive factor function models are established to adjust the dynamic model and abnormal measurements. The test results show that the standard extended Kalman filter cannot always give an optimal solution due to the influence of GNSS measurements and satellite geometry especially in the complex environment, while the proposed method improves the reliability of the integrated navigation system by adopting robust adaptive factor.

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Abbreviations

δr n :

error vectors of position (m)

δv n :

error vectors of velocity (m/s)

ψ n :

error vectors of attitude (rad/s)

∇ :

accelerometer error vector (m/s2)

f n :

vectors of specific force (N)

ε:

gyro drift (rad/s)

ω n ie :

earth rotation velocity in the n-frame (rad/s)

ω n en :

the rotation vector from the e-frame to the n-frame (rad/s)

ω n in :

the sum of the ω nie and ω nen (rad/s)

\(d{t_{{u_b}}}\) :

receiver clock bias (m)

\(d{t_{{u_d}}}\) :

receiver clock drift (m)

ρ I :

pseudo range measurements from INS (m)

ω G :

pseudo range measurements from GPS (m)

ω̇ I :

pseudo range rate measurements from INS (m/s)

ω̇ G :

pseudo range rate measurements from GPS (m/s)

INS:

inertial navigation system

GNSS:

global navigation satellite system

SGEs:

slowly growing errors

FDI:

fault detection and isolation

KF:

kalman filter

AIME:

autonomous integrity monitoring by extrapolation

MMAE:

multiple-model-based adaptive estimation

EKF:

extended kalman filter

IAE:

innovation-based adaptive estimation

RAE:

residual-based adaptive estimation

PDOP:

position dilution of precision

RAIM:

autonomous integrity monitoring

IMU:

inertial measurement unit

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Acknowledgement

This research was supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 19KJB510028), the National Natural Science Foundation of China (Grant No. 61803188) and the Doctoral Scientific Research Startup Foundation of Jinling Institute of Technology (Grant No. jit-b-201603) and Postdoctoral Foundation of Jiangsu Province (Grant No. 2021K463C).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Jinling Institute of Technology, Nanjing, 211169, China

    Youlong Wu

  2. School of Automation, Nanjing University of Science and Technology, Nanjing, 210094, China

    Youlong Wu & Shuai Chen

  3. School of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing, 211169, China

    Tingting Yin

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  1. Youlong Wu
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  2. Shuai Chen
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Correspondence to Youlong Wu.

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Wu, Y., Chen, S. & Yin, T. GNSS/INS Tightly Coupled Navigation with Robust Adaptive Extended Kalman Filter. Int.J Automot. Technol. 23, 1639–1649 (2022). https://doi.org/10.1007/s12239-022-0142-7

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