Distributed Multi-Source Spatial Data Fusion Model Construction and Performance Evaluation

2011 ◽  
Vol 460-461 ◽  
pp. 404-408
Author(s):  
Yue Shun He ◽  
Jun Zhang ◽  
Jie He
Keyword(s):  
Performance Evaluation ◽  
Data Fusion ◽  
Information Fusion ◽  
Spatial Data ◽  
Evaluation Model ◽  
Distributed Model ◽  
Fusion System ◽  
Fusion Model ◽  
Test Environment ◽  
Sensor Information

This paper mainly analyzed the principle of multi-source spatial data fusion, and expounded the multi-source spatial data fusion of the distributed model structure. The paper considers a distributed multi-sensor information fusion system factors, A performance evaluation model was established which was suitable for distributed multi-sensor information fusion system, It can estimate the system's precision, track quality, filtering quality, and the relevant between Navigation Paths and so on. Meanwhile, we had a lot of experiments by the datum which generated by the simulation test environment, experiments show that this evaluation model is valid.

Position and Attitude Information Fusion for Portable Unmanned Aerial Vehicles

2010 ◽  
Vol 439-440 ◽  
pp. 155-160
Author(s):  
Jin Jun Rao ◽  
Tong Yue Gao ◽  
Zhen Jiang ◽  
Zhen Bang Gong
Keyword(s):  
Data Fusion ◽  
Unmanned Aerial Vehicles ◽  
Original Data ◽  
State Equation ◽  
Fusion System ◽  
Fusion Model ◽  
Discrete Equations ◽  
Aerial Vehicles ◽  
Application Potential ◽  
Accurate Position

Portable Unmanned Aerial Vehicles (PUAVs) present an enormous application potential, and the real time accurate position and attitude information is the basis of autonomous flight of PUAVs. In order to obtain comprehensive and accurate position and attitude data of PUAVs in flight, focusing on the common sensors configuration of PUAVs, each type of sensor’s characteristic is analyzed, and the data fusion problem of SINS/GPS/Compass combination is presented and studied in this paper. Firstly, the error expressions of MEMS inertia sensors, attitude, velocity and position are researched and derived, and the state equation and observation equation are built, and the discrete equations are derived for computer implementation, so the data fusion model for Kalman Filter fusion algorithms is presented. Then, the data fusion system and algorithms are implemented in software, and the flight data obtained in flight experiment is fed to the software for data fusion. The comparison between original data and fusional data shows that SINS/GPS/Compass data fusion system can promote accuracy of position and attitude markedly.

scholarly journals A Hybrid Data-Fusion System by Integrating CFD and PNN for Structural Damage Identification

2021 ◽  
Vol 11 (17) ◽  
pp. 8272
Author(s):  
Chun Fu ◽  
Shao-Fei Jiang
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Structural Damage ◽  
Damage Identification ◽  
Fusion System ◽  
Fusion Model ◽  
Decision Level ◽  
Hybrid Data ◽  
Level Model ◽  
Level Fusion

Recently, a variety of intelligent structural damage identification algorithms have been developed and have obtained considerable attention worldwide due to the advantages of reliable analysis and high efficiency. However, the performances of existing intelligent damage identification methods are heavily dependent on the extracted signatures from raw signals. This will lead to the intelligent damage identification method becoming the optimal solution for actual application. Furthermore, the feature extraction and neural network training are time-consuming tasks, which affect the real-time performance in identification results directly. To address these problems, this paper proposes a new intelligent data fusion system for damage detection, combining the probabilistic neural network (PNN), data fusion technology with correlation fractal dimension (CFD). The intelligent system consists of three modules (models): the eigen-level fusion model, the decision-level fusion model and a PNN classifier model. The highlight points of this system are these three intelligent models specialized in certain situations. The eigen-level model is specialized in the case of measured data with enormous samples and uncertainties, and for the case of confidence level of each sensor is determined ahead, the decision-level model is the best choice. The single PNN model is considered only when the data collected is somehow limited, or few sensors have been installed. Numerical simulations of a two-span concrete-filled steel tubular arch bridge in service and a seven-storey steel frame in laboratory were used to validate the hybrid system by identifying both single- and multi-damage patterns. The results show that the hybrid data-fusion system has excellent performance of damage identification, and also has superior capability of anti-noise and robustness.

scholarly journals Sensors Information Fusion System with Fault Detection Based on Multi-Manifold Regularization Neighborhood Preserving Embedding

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1440
Author(s):  
Jianping Wu ◽  
Bin Jiang ◽  
Hongtian Chen ◽  
Jianwei Liu
Keyword(s):  
Fault Detection ◽  
Information Fusion ◽  
High Speed ◽  
Euclidean Distance ◽  
Manifold Regularization ◽  
Fusion System ◽  
Electrical Drive ◽  
High Speed Trains ◽  
Sensor Information ◽  
Drive Systems

Electrical drive systems play an increasingly important role in high-speed trains. The whole system is equipped with sensors that support complicated information fusion, which means the performance around this system ought to be monitored especially during incipient changes. In such situation, it is crucial to distinguish faulty state from observed normal state because of the dire consequences closed-loop faults might bring. In this research, an optimal neighborhood preserving embedding (NPE) method called multi-manifold regularization NPE (MMRNPE) is proposed to detect various faults in an electrical drive sensor information fusion system. By taking locality preserving embedding into account, the proposed methodology extends the united application of Euclidean distance of both designated points and paired points, which guarantees the access to both local and global sensor information. Meanwhile, this structure fuses several manifolds to extract their own features. In addition, parameters are allocated in diverse manifolds to seek an optimal combination of manifolds while entropy of information with parameters is also selected to avoid the overweight of single manifold. Moreover, an experimental test based on the platform was built to validate the MMRNPE approach and demonstrate the effectiveness of the fault detection. Results and observations show that the proposed MMRNPE offers a better fault detection representation in comparison with NPE.

scholarly journals AI-Based Sensor Information Fusion for Supporting Deep Supervised Learning

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1345 ◽  
Author(s):  
Carson Leung ◽  
Peter Braun ◽  
Alfredo Cuzzocrea
Keyword(s):  
Supervised Learning ◽  
Information Fusion ◽  
Smart Cities ◽  
Satellite System ◽  
Sensor Data ◽  
Fusion System ◽  
Sensor Information ◽  
Global Navigation Satellite ◽  
Effective Integration ◽  
The Internet Of Things

In recent years, artificial intelligence (AI) and its subarea of deep learning have drawn the attention of many researchers. At the same time, advances in technologies enable the generation or collection of large amounts of valuable data (e.g., sensor data) from various sources in different applications, such as those for the Internet of Things (IoT), which in turn aims towards the development of smart cities. With the availability of sensor data from various sources, sensor information fusion is in demand for effective integration of big data. In this article, we present an AI-based sensor-information fusion system for supporting deep supervised learning of transportation data generated and collected from various types of sensors, including remote sensed imagery for the geographic information system (GIS), accelerometers, as well as sensors for the global navigation satellite system (GNSS) and global positioning system (GPS). The discovered knowledge and information returned from our system provides analysts with a clearer understanding of trajectories or mobility of citizens, which in turn helps to develop better transportation models to achieve the ultimate goal of smarter cities. Evaluation results show the effectiveness and practicality of our AI-based sensor information fusion system for supporting deep supervised learning of big transportation data.

Techniques and Methods of Spatial Data Fusion

2012 ◽  
Vol 263-266 ◽  
pp. 3274-3278
Author(s):  
Hui Ming Yu ◽  
Jian Zhong Guo ◽  
Yi Cheng ◽  
Qian Lou
Keyword(s):  
Data Fusion ◽  
Information Fusion ◽  
Spatial Data ◽  
Data Model ◽  
Semantic Information ◽  
Utilization Efficiency ◽  
Vector Data ◽  
Raster Data ◽  
Information Precision ◽  
Spatial Data Integration

Spatial data fusion is an important method of spatial data acquisition. The aim of multisource spatial data integration and fusion is to improve the information precision and information's utilization efficiency. Vector and raster are the two main spatial data structures. This article discusses vector data fusion from of data model fusion, semantic information fusion and coordinates unification, reviews the main methods of raster data fusion and discusses the key technologies of vector and raster data fusion, and proposes the future developments of spatial data fusion technique.

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