A Hybrid Data Fusion Approach to AI-Assisted Indirect Centralized Integrated SINS/GNSS Navigation System During GNSS Outage

Abdolkarimi, Elahe Sadat; Mosavi, Mohammad Reza; Rafatnia, Sadra; Martín, Diego

doi:10.1109/access.2021.3096422

Cited by 13 publications

(3 citation statements)

References 28 publications

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“…Shen et al [36] proposed a hybrid navigation strategy utilizing the self-learning square root volume Kalman filter. Abdolkarimi et al [37] applied the Iterative Empirical Mode Decomposition (EMD) interval threshold denoising method to enhance the smoothness and accuracy of SINS measurements. They devised an improved Indirect Centralized Correlation Entropy Kalman Filter, incorporating a novel fuzzy parameter adjustment method for adaptive estimation parameter changes.…”

Section: Algorithms Based On Kalman Filtermentioning

confidence: 99%

Research on fusion navigation framework and algorithm based on fully-connected neural network

Xu,

Liu,

Zhu

et al. 2024

Meas. Sci. Technol.

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Fusion navigation and positioning have evolved into crucial technologies and methodologies within navigation systems. Currently, the majority of combined positioning frameworks employ Kalman filtering algorithms for data fusion. To explore more efficient and high-precision fusion architectures and algorithms, we introduce a fusion navigation framework based on a fully-connected neural network (FCNN). Initially, we conducted an analysis of existing fusion positioning technology, data fusion algorithms, and the application of artificial intelligence algorithms in navigation positioning. Drawing insights from the federated Kalman filter (FKF) architecture, FCNN, and attention mechanism, we propose a fusion navigation framework centred on FCNN. Finally, fixed-point and trajectory determination experiments were carried out in both open and semi-shielded environments. The results demonstrate that, compared to the traditional FKF architecture, the FCNN fusion navigation framework, coupled with the attention mechanism fusion algorithm, effectively accommodates data, mitigates errors, and achieves superior positioning accuracy.

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Section: Algorithms Based On Kalman Filtermentioning

confidence: 99%

Research on fusion navigation framework and algorithm based on fully-connected neural network

Xu,

Liu,

Zhu

et al. 2024

Meas. Sci. Technol.

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“…The structure depicted in Figure 2 is generally known as an indirect, or error-state, Kalman filter. It is the common method for implementing IMU-aided navigation solutions [ 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. The formulation below assumes that IMU data arrive at a higher rate than either GNSS or LiDAR measurements.…”

Section: Pose Estimation Using Gnss Imu and Lidar Measurementsmentioning

confidence: 99%

LiDAR-Stabilised GNSS-IMU Platform Pose Tracking

D’Adamo

Phillips

McĂree

2022

Sensors

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The requirement to estimate the six degree-of-freedom pose of a moving platform frequently arises in automation applications. It is common to estimate platform pose by the fusion of global navigation satellite systems (GNSS) measurements and translational acceleration and rotational rate measurements from an inertial measurement unit (IMU). This paper considers a specific situation where two GNSS receivers and one IMU are used and gives the full formulation of a Kalman filter-based estimator to do this. A limitation in using this sensor set is the difficulty of obtaining accurate estimates of the degree of freedom corresponding to rotation about the line passing through the two GNSS receiver antenna centres. The GNSS-aided IMU formulation is extended to incorporate LiDAR measurements in both known and unknown environments to stabilise this degree of freedom. The performance of the pose estimator is established by comparing expected LiDAR range measurements with actual range measurements. Distributions of the terrain point-to-model error are shown to improve from 0.27m mean error to 0.06m when the GNSS-aided IMU estimator is augmented with LiDAR measurements. This precision is marginally degraded to 0.14m when the pose estimator is operated in an a prior unknown environment.

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“…(3) Concurrency Concurrency during program execution is a very common behavior in computer networks. For example, multiple nodes in the same distributed system can simultaneously work on some shared resource, such as a database or shared storage [15][16].…”

Section: Features Of Distributed Systemsmentioning

confidence: 99%

Distributed System Optimization Analysis Based on Data Fusion and Data Transmission Method

2021

DPS

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With the continuous development of network technology and the release of various software products, people's lives are increasingly connected with the Internet. With the rapid increase in the number of users of software products, the processing requirements of servers are also increasing rapidly, and distributed systems begin to play an important role. The purpose of this paper is to analyze the optimization of distributed systems based on data fusion and transmission methods. First, it reveals the research and development background of the subject and its importance in scientific research, and studies the data acquisition technology based on RabbitMQ. Some new RabbitMQ implementation functions are proposed in the log collection service, and the RabbitMQ message processing method inside the model is analyzed and optimized. After comparative experiments in this paper, it is confirmed that the optimized RabbitMQ can support more log generators and thus have more efficient processing performance. Through experimental comparison and data analysis, it can be found that in this use case, when there is only one producer, the maximum delivery speed of the server before optimization can be reached faster, with a speed of 3800msg/s, while the optimized server with the maximum delivery speed is 3950msg/s, 2.3% higher than the highest delivery speed.

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A Hybrid Data Fusion Approach to AI-Assisted Indirect Centralized Integrated SINS/GNSS Navigation System During GNSS Outage

Cited by 13 publications

References 28 publications

Research on fusion navigation framework and algorithm based on fully-connected neural network

Research on fusion navigation framework and algorithm based on fully-connected neural network

LiDAR-Stabilised GNSS-IMU Platform Pose Tracking

Distributed System Optimization Analysis Based on Data Fusion and Data Transmission Method

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