Deep Learning Based Vehicle-Mounted Environmental Context Awareness via GNSS Signal

Zhu, Feng; Luo, Kegan; Tao, Xianlu; Zhang, Xiaohong

doi:10.1109/tits.2024.3350874

Cited by 2 publications

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the prediction of GNSS navigation results in integrated navigation, there are mainly two ways, one is based on GNSS historical data [42], and the other is using the correlation between GNSS and INS navigation results [43]. When using GNSS historical data to predict navigation results, it is required that the training data should contain all the movement conditions of the USV as much as possible.…”

Section: The Attention Gru Network Aiding Gnss/ins Integrated Navigat...mentioning

confidence: 99%

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

Lou,

Liu,

et al. 2024

Phys. Scr.

View full text Add to dashboard Cite

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.

show abstract

Section: The Attention Gru Network Aiding Gnss/ins Integrated Navigat...mentioning

confidence: 99%

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

Lou,

Liu,

et al. 2024

Phys. Scr.

View full text Add to dashboard Cite

show abstract

A survey of machine learning techniques for improving Global Navigation Satellite Systems

Mohanty,

Gao

2024

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Global Navigation Satellite Systems (GNSS)-based positioning plays a crucial role in various applications, including navigation, transportation, logistics, mapping, and emergency services. Traditional GNSS positioning methods are model-based, utilizing satellite geometry and the known properties of satellite signals. However, model-based methods have limitations in challenging environments and often lack adaptability to uncertain noise models. This paper highlights recent advances in machine learning (ML) and its potential to address these limitations. It covers a broad range of ML methods, including supervised learning, unsupervised learning, deep learning, and hybrid approaches. The survey provides insights into positioning applications related to GNSS, such as signal analysis, anomaly detection, multi-sensor integration, prediction, and accuracy enhancement using ML. It discusses the strengths, limitations, and challenges of current ML-based approaches for GNSS positioning, providing a comprehensive overview of the field.

show abstract

Deep Learning Based Vehicle-Mounted Environmental Context Awareness via GNSS Signal

Cited by 2 publications

References 39 publications

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

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

A survey of machine learning techniques for improving Global Navigation Satellite Systems

Contact Info

Product

Resources

About