Research on GNSS/INS Tightly Coupled Integrity Monitoring Method Based on State Augmentation Error Modeling.
Saved in:
| Title: | Research on GNSS/INS Tightly Coupled Integrity Monitoring Method Based on State Augmentation Error Modeling. |
|---|---|
| Authors: | Tang, Xinhua1 (AUTHOR) xinhua.tang@seu.edu.cn, Fang, Xiaoyu1,2 (AUTHOR), Huang, Fei2,3 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1564. 29p. |
| Subjects: | Markov processes, Covariance matrices, Measurement errors, Data integrity, Inertial navigation systems, Global Positioning System, Automotive navigation systems |
| Abstract: | Highlights: What are the main findings? A tightly coupled GNSS/INS integrity monitoring method is developed by introducing time-correlated GNSS error sources into the filter state through Gauss–Markov state augmentation. PSD-constrained parameter tuning provides more consistent covariance estimates for the MHSS framework, reducing protection level underestimation in the tested obstructed scenario. What are the implications of the main findings? The proposed covariance construction improves estimation consistency under colored GNSS measurement errors without noticeably degrading positioning accuracy. Field and controlled fault-injection results indicate improved empirical HPL/HPE bounding while maintaining system availability in the tested vehicular scenario. In urban environments with signal blockage and multipath effects, GNSS observation errors often exhibit temporal correlation. The Gaussian white noise assumption adopted in conventional tightly coupled Kalman filtering is prone to model mismatch under such conditions, which may lead to an underestimation of state uncertainty and consequently cause the protection level (PL) to fail to reliably bound the true positioning error. To address this issue, this paper proposes a tightly coupled GNSS/INS integrity monitoring method based on state augmentation and frequency-domain constrained parameter tuning. The method introduces first-order Gauss-Markov processes (GMP) to model major time-correlated error sources, including residual ephemeris and clock errors, residual tropospheric delay, and code multipath, by augmenting them into the filter state for joint estimation. The model parameters are further conservatively tuned based on power spectral density (PSD) envelope constraints to obtain more consistent covariance estimates. Based on this, the covariance output from the augmented filter is incorporated into the multiple hypothesis solution separation (MHSS) framework, enabling the protection level computation to better match the actual error statistics. Experimental results using vehicular field test data show that the proposed method effectively improves estimation consistency and significantly reduces the risk of PL underestimation in degraded environments. Furthermore, it achieves reliable bounding of horizontal positioning errors without noticeable degradation in positioning accuracy, while maintaining good system availability. These results demonstrate the effectiveness of covariance construction based on physical error modeling and PSD envelope constraints for integrity monitoring in complex environments. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Highlights: What are the main findings? A tightly coupled GNSS/INS integrity monitoring method is developed by introducing time-correlated GNSS error sources into the filter state through Gauss–Markov state augmentation. PSD-constrained parameter tuning provides more consistent covariance estimates for the MHSS framework, reducing protection level underestimation in the tested obstructed scenario. What are the implications of the main findings? The proposed covariance construction improves estimation consistency under colored GNSS measurement errors without noticeably degrading positioning accuracy. Field and controlled fault-injection results indicate improved empirical HPL/HPE bounding while maintaining system availability in the tested vehicular scenario. In urban environments with signal blockage and multipath effects, GNSS observation errors often exhibit temporal correlation. The Gaussian white noise assumption adopted in conventional tightly coupled Kalman filtering is prone to model mismatch under such conditions, which may lead to an underestimation of state uncertainty and consequently cause the protection level (PL) to fail to reliably bound the true positioning error. To address this issue, this paper proposes a tightly coupled GNSS/INS integrity monitoring method based on state augmentation and frequency-domain constrained parameter tuning. The method introduces first-order Gauss-Markov processes (GMP) to model major time-correlated error sources, including residual ephemeris and clock errors, residual tropospheric delay, and code multipath, by augmenting them into the filter state for joint estimation. The model parameters are further conservatively tuned based on power spectral density (PSD) envelope constraints to obtain more consistent covariance estimates. Based on this, the covariance output from the augmented filter is incorporated into the multiple hypothesis solution separation (MHSS) framework, enabling the protection level computation to better match the actual error statistics. Experimental results using vehicular field test data show that the proposed method effectively improves estimation consistency and significantly reduces the risk of PL underestimation in degraded environments. Furthermore, it achieves reliable bounding of horizontal positioning errors without noticeable degradation in positioning accuracy, while maintaining good system availability. These results demonstrate the effectiveness of covariance construction based on physical error modeling and PSD envelope constraints for integrity monitoring in complex environments. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101564 |
