Abstract

With the development of multi-constellation multi-frequency Global Navigation Satellite Systems (GNSS), more and more observations are available for tightly coupled GNSS/Inertial Navigation System (INS) integration. Concerning the accuracy, robustness, and computational burden issues in the integration, we proposed a robust and computationally efficient implementation. The new tight integration model uses pseudorange, Doppler and carrier phase simultaneously, to achieve the maximum possible navigation accuracy for a single receiver. The resultant high-dimensional observation vector is then processed by a sequential Kalman Filter (KF) to improve the computational efficiency in the measurement update step. Based on the innovation of the sequential KF, a robust estimation method with Gaussian test is further devised to detect and adapt the faults in individual GNSS channels. Two field vehicular tests are conducted to evaluate the performance improvements of the proposed method, compared with loose coupling and conventional tight coupling. Test results in favorable environments indicate that the proposed method can significantly improve the velocity and attitude accuracy by 69.42% and 47.16% over loose coupling and by 64.75% and 30.88% over conventional tight coupling, respectively. Moreover, the computational efficiency is also improved by about 53.09% for the proposed method, compared with batch KF processing. In GNSS challenging environments, the proposed method also shows superiority in terms of velocity and attitude accuracy, and better bridging capability during the GNSS partial or complete outages. These results demonstrate that the proposed method is able to provide a more robust and accurate solution in real-time vehicular navigation.

Highlights

  • Accurate and reliable navigation information is a fundamental basis of many vehicular applications such as autonomous driving, location based services, and traffic management

  • We proposed a tightly coupled Global Navigation Satellite Systems (GNSS)/INS integration algorithm using robust for accurate vehicular navigation

  • Apart from pseudorange and Doppler used in traditional tight sequential Kalman Filter (KF) for accurate vehicular navigation

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Summary

Introduction

Accurate and reliable navigation information is a fundamental basis of many vehicular applications such as autonomous driving, location based services, and traffic management. PPP can work in a single receiver case with precise orbit and clock products provided by a global reference network (e.g., the International GNSS Service network) It generally requires a comparatively long time to become (re)convergent, which is not tolerable in some real-time applications. KF based on the Mahalanobis distances and Chi-square test is devised to detect and adapt the outliers [25] This method possesses some features of both the above two kinds and its efficacy was demonstrated in loosely-coupled GNSS/INS integration [26]. With the advent of multi-constellation multi-frequency GNSS, multi-type observations such as pseudorange, Doppler, and carrier phase of multiple satellites are available for measurement update in TC of GNSS/INS integration. All type observations, including pseudorange, Doppler, and carrier phase, are fused with INS to achieve the maximum possible navigation accuracy for a single receiver (TC-PDC).

GNSS Observations
Dynamic Model
Measurement Model
Robust Sequential KF
Sequential KF
Robust Estimation Based on Innovation Test
Test and Results
Two sets
Satellite Availability
Performance Comparison
Performance of Robust Estimation
Navigation errors andTC-PDC
Computational Efficiency Comparison
Foliage
Performance in GNSS Challenging Environments
Conclusions
Full Text
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