A novel approach for aiding unscented Kalman filter for bridging GNSS outages in integrated navigation systems

Bitar, N. Al; Gavrilov, A. I.

doi:10.1002/navi.435

Cited by 14 publications

(4 citation statements)

References 30 publications

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“…For GNSS signal occlusion, several studies have used machine learning to predict and correct Kalman filtered measurements to improve positioning and velocity accuracy [28,29]. Alternatively, machine learning cam be used for GNSS position prediction to reduce abrupt changes in the measurement covariance matrix during GNSS signal occlusion [30].…”

Section: Integrated Navigation For Occluded Environmentsmentioning

confidence: 99%

Robust factor graph optimisation method for shipborne GNSS/INS integrated navigation system

Hu,

Li,

Liu

2023

IET Radar Sonar & Navi

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Robust Global Navigation Satellite System (GNSS) factors are introduced into a factor graph optimisation based integrated navigation system to address the challenge of occluded GNSS signals during ship navigation, which leads to increased errors in positioning results. To enhance the robustness of the GNSS tracking loop, a vector tracking method is applied to receiver tracking loop. Then, the Mahalanobis distance was employed to assess the pseudorange residual and identify and reject signals that exhibit anomalies. Specifically, the pseudorange residual is computed as the difference between the predicted pseudorange of the GNSS receiver and the measured pseudorange. Using the historical information in the window, robust GNSS factors were constructed for use in the factor graph. The robust factor graph optimisation method for a shipborne GNSS/Inertial Navigation System integrated navigation system was implemented by constructing robust GNSS factors and Inertial Measurement Unit factors. The experimental results confirm that the positioning accuracy of the proposed method is superior to those of the factor graph optimization and extended Kalman filter.

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Section: Integrated Navigation For Occluded Environmentsmentioning

confidence: 99%

Robust factor graph optimisation method for shipborne GNSS/INS integrated navigation system

Hu,

Li,

Liu

2023

IET Radar Sonar & Navi

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“…are introduced. A fairly wide class of methods is based on the use of satellite measurements only, including high-precision phase measurements of pseudo-range [1,2,8,9]. There are obvious disadvantages -the lack of the possibility of working in autonomous mode (in case of loss of GPS signals) and low accuracy in mountain relief conditions, at a high level of atmospheric interference, at multiple reflections in urban conditions, as well as due to inevitable instrumental errors of the satellite transmitter and object receiver.…”

Section: Introductionmentioning

confidence: 99%

Stochastic filtering of unmanned objects parameters in conditions of uncertainty

Sokolov,

Reshetnikova,

Marshakov

et al. 2024

Optical Sensing and Detection VIII

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The approach to solving the problem of positioning unmanned transport objects, in particular, railway transport, in conditions of measurement interference uncertainty is considered. Today, a large number of urban (as well as railway) infrastructure facilities generate an unpredictable nature of disturbances acting on navigation sensors of unmanned objects. In this case, the use of both satellite measurements and various sensors located on the site (in particular, a video surveillance system) often becomes impossible. A stable high-precision solution to the problem of positioning unmanned transport objects becomes necessary. To solve this problem, the article proposes to use autonomous linear motion parameter meters as sensitive elements of the navigation complex. It is proposed to use a Kalman filter and a robust filtration method to process noisy measurements, including those with uncertain probability characteristics.

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“…Global Navigation Satellite systems (GNSS) can provide accurate position and velocity information in outdoor environments, and its errors do not accumulate over time [1]. The disadvantages are that it can only provide less accuracy attitude information, the output frequency is low (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), and it is vulnerable to environmental interference. In contrast, the Inertial Navigation System (INS) is less dependent on the environment, and relies entirely on the angular velocity and acceleration information that is measured by the Inertial Measurement Unit (IMU), which can provide high-frequency navigation information [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…The remaining higher-order terms are ignored, and their performance depends on the degree of local nonlinearity. The unscented Kalman filter (UKF) was proposed to further improve the performance under nonlinear systems by making the nonlinear system equations applicable to linear assumptions through lossless transformations [9,10]. By approximating the posterior probability density of the state with a series of deterministic samples, the problem of the EKF accuracy dispersion under a highly nonlinear system is avoided.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for AI-Assisted INS/GNSS Navigation System Based on CNN-GRU and CKF during GNSS Outage

2022

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In the fields of positioning and navigation, the integrated inertial navigation system (INS)/global navigation satellite systems (GNSS) are frequently employed. Currently, high-precision INS typically utilizes fiber optic gyroscopes (FOGs) and quartz flexural accelerometers (QFAs) rather than MEMS sensors. But when GNSS signals are not available, the errors of high-precision INS also disperse rapidly, similar to MEMS-INS when GNSS signals would be unavailable for a long time, leading to a serious degradation of the navigation accuracy. This paper presents a new AI-assisted method for the integrated high-precision INS/GNSS navigation system. The position increments during GNSS outage are predicted by the convolutional neural network-gated recurrent unit (CNN-GRU). In the process, the CNN is utilized to quickly extract the multi-dimensional sequence features, and GRU is used to model the time series. In addition, a new real-time training strategy is proposed for practical application scenarios, where the duration of the GNSS outage time and the motion state information of the vehicle are taken into account in the training strategy. The real road test results verify that the proposed algorithm has the advantages of high prediction accuracy and high training efficiency.

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A novel approach for aiding unscented Kalman filter for bridging GNSS outages in integrated navigation systems

Cited by 14 publications

References 30 publications

Robust factor graph optimisation method for shipborne GNSS/INS integrated navigation system

Robust factor graph optimisation method for shipborne GNSS/INS integrated navigation system

Stochastic filtering of unmanned objects parameters in conditions of uncertainty

A Novel Method for AI-Assisted INS/GNSS Navigation System Based on CNN-GRU and CKF during GNSS Outage

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