Variational Bayesian-based robust adaptive filtering for GNSS/INS tightly coupled positioning in urban environments

Ma, Chun; Pan, Shuguo; Gao, Wang; Wang, Hao; Liu, Liwei

doi:10.1016/j.measurement.2023.113668

Cited by 6 publications

(2 citation statements)

References 43 publications

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“…The second challenge lies in the research on robust algorithms for observational data in complex environments. For GNSS/INS and UWB/INS, Kalman filtering is the most widely used parameter estimation method in the data-processing process [22]. However, due to the impact of complex sky and ground environments, the pseudorange gross errors of GNSS and NLOS (Non-Line-of-Sight) errors of UWB often affect the model accuracy, leading to unidentifiable biases in the filtering process [23,24].…”

Section: Related Workmentioning

confidence: 99%

Semi-Tightly Coupled Robust Model for GNSS/UWB/INS Integrated Positioning in Challenging Environments

Sun,

Gao,

Tao

et al. 2024

Remote Sensing

Self Cite

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Currently, the integration of the Global Navigation Satellite System (GNSS), Ultra-Wideband (UWB), and Inertial Navigation System (INS) has become a reliable positioning method for outdoor dynamic vehicular and airborne applications, enabling high-precision and continuous positioning in complex environments. However, environmental interference and limitations of single positioning sources pose challenges. Especially in areas with limited access to satellites and UWB base stations, loosely coupled frameworks for GNSS/INS and UWB/INS are insufficient to support robust estimation. Furthermore, within a tightly coupled framework, parameter estimations from different sources can interfere with each other, and errors in computation can easily contaminate the entire positioning estimator. To balance robustness and stability in integrated positioning, this paper proposes a comprehensive quality control method. This method is based on the semi-tightly coupled concept, utilizing the INS position information and considering the dilution of precision (DOP) skillfully to achieve complementary advantages in GNSS/UWB/INS integrated positioning. In this research, reliable position and variance information obtained by INS are utilized to provide a priori references for a robust estimation of the original data from GNSS and UWB, achieving finer robustness without increasing system coupling, which fully demonstrates the advantages of semi-tight integration. Based on self-collected data, the effectiveness and superiority of the proposed quality control strategy are validated under severely occluded environments. The experimental results demonstrate that the semi-tightly coupled robust estimation method proposed in this paper is capable of accurately identifying gross errors in GNSS and UWB observation data, and it has a significant effect on improving positioning accuracy and smoothing trajectories. Additionally, based on the judgment of the DOP, this method can ensure the output of continuous and reliable positioning results in complex and variable environments. Verified by actual data, under the conditions of severe sky occlusion and NLOS (Non-Line-of-Sight), compared with the loosely coupled GNSS/INS, the positioning accuracy in the E, N, U directions of the semi-tight coupled GNSS/INS proposed in this paper has improved by 37%, 46%, and 28%. Compared with the loosely coupled UWB/INS, the accuracy in the E and N directions of the semi-tight coupled UWB/INS has improved by 60% and 34%. In such environments, GNSS employs the RTD (Real-Time Differential) algorithm, UWB utilizes the two-dimensional plane-positioning algorithm, and the positioning accuracy of the semi-tight coupled robust model of GNSS/UWB/INS in the E, N, U directions is 0.42 m, 0.55 m, and 3.20 m respectively.

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Section: Related Workmentioning

confidence: 99%

Semi-Tightly Coupled Robust Model for GNSS/UWB/INS Integrated Positioning in Challenging Environments

Sun,

Gao,

Tao

et al. 2024

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…First of all, Adaptive Kalman filtering (AKF) [21], cubature Kalman filtering (CKF) [22] and extended Kalman filtering (EKF) [23] have been tried to replace the traditional Kalman filter for processing system state changes. Then, maximum likelihood estimation [24], variational Bayesian estimation [25], and convolutional neural networks (CNN) [26] are also used for Kalman filter optimization to dynamically approximate measurement noise. Finally, other filtering methods [27] are also considered as integrated navigation filters.…”

Section: Introductionmentioning

confidence: 99%

A robust integrated navigation optimization method for USV in signal occlusion environment

Lou,

Liu,

et al. 2024

Phys. Scr.

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Unmanned surface vehicles (USV) can use global navigation satellite systems (GNSS) and inertial navigation systems (INS) for combined positioning and navigation. However, buildings such as port facilities and bridges blocking GNSS signals will increase the error in the discriminator output in the GNSS vector tracking loop and reduce positioning accuracy. Meanwhile, due to the cumulative error in the inertial navigation system, the credibility of the navigation results when the signal is blocked is further reduced. In this regard, this study proposes a robust integrated navigation optimization method. Specifically, the RTS smoothing optimized Kalman filter is used to constrain the carrier phase error and code phase error output by the discriminator, which can dynamically adjust the gain of the vector tracking loop, thereby improving the signal tracking capability. Simultaneously, the prediction results of the gated recurrent unit (GRU) network optimized based on the attention mechanism are combined with the inertial navigation system to improve navigation accuracy. Furthermore, an adaptive Kalman filter is utilized as the integrated navigation filter. The actual path of the carrier refers to the navigation solution of the existing receiver. In the open environment, the proposed optimization method reduces horizontal positioning error and speed error by 44.7% and 37.1% respectively compared with existing methods. Simultaneously, it can effectively improve the robustness of positioning in signal obstruction environments. The proposed integrated navigation method provides new possibilities for optimizing USV navigation solutions.

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Environmental information-assisted intelligent fusion localization for vehicles in urban area

Xu,

Yu,

et al. 2024

Measurement

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Variational Bayesian-based robust adaptive filtering for GNSS/INS tightly coupled positioning in urban environments

Cited by 6 publications

References 43 publications

Semi-Tightly Coupled Robust Model for GNSS/UWB/INS Integrated Positioning in Challenging Environments

Semi-Tightly Coupled Robust Model for GNSS/UWB/INS Integrated Positioning in Challenging Environments

A robust integrated navigation optimization method for USV in signal occlusion environment

Environmental information-assisted intelligent fusion localization for vehicles in urban area

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