Artificial Intelligence Based Methods for Accuracy Improvement of Integrated Navigation Systems During GNSS Signal Outages: An Analytical Overview

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

doi:10.1134/s2075108720010022

Cited by 18 publications

(8 citation statements)

References 36 publications

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“…However, with the densification of urban buildings and three-dimensional traffic, in some sections (such as urban canyon, underground tunnel, and lower pavement of urban interchange, etc. ), satellite signals are partially or completely blocked for a long time, resulting in the multipath effect or signal interruption of GNSS, which makes GNSS unable to provide accurate positioning information [3] . In order to solve the above problems, multi-sensor data fusion [4] technology is commonly used.…”

Section: Introductionmentioning

confidence: 99%

High Precision Vehicle Location Method Based on Multi-sensor Fusion in the Urban Complex Environment

Li,

Zhuang

2024

J. Phys.: Conf. Ser.

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In this paper, a high-precision vehicle positioning method is proposed, which can achieve centimeter-level positioning accuracy in a complex environment. Based on the information of multiple sensors (GNSS, Lidar, and IMU), the multi-sensor fusion framework is used to fuse the positioning measurements from different sources with Kalman filter, which achieves high-precision positioning in the case of GNSS disconnection, the accuracy and robustness of the positioning system are significantly improved, and the effectiveness of the method is verified. The average RMS accuracy of 0.07m is realized, which meets the requirements of high-precision positioning for autonomous vehicles.

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Section: Introductionmentioning

confidence: 99%

High Precision Vehicle Location Method Based on Multi-sensor Fusion in the Urban Complex Environment

Li,

Zhuang

2024

J. Phys.: Conf. Ser.

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“…The RLS‐SVM is used to predict the pseudo‐GNSS position during GNSS outages similar to the previously proposed system in Al Bitar et al. (2020). The inputs of the RLS‐SVM model are the specific force, velocity, and yaw information.…”

Section: Introductionmentioning

confidence: 99%

“…Much research has been conducted to investigate the use of AI-based techniques to bridge GNSS signal outages in INS/GNSS systems. Researchers have utilized various approaches for combining the AI module(s) with the rest of the INS/GNSS system (Al Bitar et al, 2020). suggested the replacement of KF by AI module using the so-called position update architecture (PUA).…”

Section: Introductionmentioning

confidence: 99%

“…proposed robust least squares support vector machine (RLS-SVM)-aided fusion methodology for INS during GNSS outages. The RLS-SVM is used to predict the pseudo-GNSS position during GNSS outages similar to the previously proposed system in Al Bitar et al (2020). The inputs of the RLS-SVM model are the specific force, velocity, and yaw information.…”

Section: Introductionmentioning

confidence: 99%

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

Bitar

Gavrilov

2021

Navigation

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Aiming to improve the position and velocity precision of the INS/GNSS system during GNSS outages, a novel system that combines unscented Kalman filter (UKF) and nonlinear autoregressive neural networks with external inputs (NARX) is proposed. The NARX‐based module is utilized to predict the measurement updates of UKF during GNSS outages. A new offline approach for selecting the optimal inputs of NARX networks is suggested and tested. This approach is based on mutual information (MI) theory for identifying the inputs that influence each of the outputs (the measurement updates of UKF) and lag‐space estimation (LSE) for investigating the dependency of these outputs on the past values of the inputs and the outputs. The performance of the proposed system is verified experimentally using a real dataset. The comparison results indicate that the NARX‐aided UKF outperforms other methods that use different input configurations for neural networks.

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“…An ensemble convolutional neural network model [ 36 ] was proposed to solve the problem of information losses during the information fusion. Hybrid fusion approaches are a comprehensive scheme, which combine different fusion methods such as fuzzy reasoning, D–S evidence theory, and neural networks to complete complex fusion tasks [ 37 , 38 , 39 , 40 ]. An adaptive fuzzy extended Kalman filter [ 41 ] was developed for attitude estimation with the outputs of strap-down IMU (gyroscopes and accelerometers) and strap-down magnetometer.…”

Section: Introductionmentioning

confidence: 99%

Adaptive-Neuro-Fuzzy-Based Information Fusion for the Attitude Prediction of TBMs

Zhu

Han

et al. 2020

Sensors

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In a tunneling boring machine (TBM), to obtain the attitude in real time is very important for a driver. However, the current laser targeting system has a large delay before obtaining the attitude. So, an adaptive-neuro-fuzzy-based information fusion method is proposed to predict the attitude of a laser targeting system in real time. In the proposed method, a dual-rate information fusion is used to fuse the information of a laser targeting system and a two-axis inclinometer, and then obtain roll and pitch angles with a higher rate and provide a smoother attitude prediction. Considering that a measurement error exists, the adaptive neuro-fuzzy inference system (ANFIS) is proposed to model the measurement error, and then the ANFIS-based model is combined with the dual-rate information fusion to achieve high performance. Experimental results show the ANFIS-based information fusion can provide higher real-time performance and accuracy of the attitude prediction. Experimental results also verify that the ANFIS-based information fusion can solve the problem of the laser targeting system losing signals.

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Artificial Intelligence Based Methods for Accuracy Improvement of Integrated Navigation Systems During GNSS Signal Outages: An Analytical Overview

Cited by 18 publications

References 36 publications

High Precision Vehicle Location Method Based on Multi-sensor Fusion in the Urban Complex Environment

High Precision Vehicle Location Method Based on Multi-sensor Fusion in the Urban Complex Environment

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

Adaptive-Neuro-Fuzzy-Based Information Fusion for the Attitude Prediction of TBMs

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