A Likelihood-Based Data Fusion Model for the Integration of Multiple Sensor Data: A Case Study with Vision and Lidar Sensors

2016 ◽  
pp. 489-500 ◽  
Author(s):  
Jun Jo ◽  
Yukito Tsunoda ◽  
Bela Stantic ◽  
Alan Wee-Chung Liew
Keyword(s):  
Data Fusion ◽  
Sensor Data ◽  
Fusion Model ◽  
Multiple Sensor

Adaptation of the JDL Model for Multi-Sensor National Cyber Security Data Fusion

2016 ◽  
Vol 6 (3) ◽  
pp. 17-30 ◽  
Author(s):  
Ignatius Swart ◽  
Barry Irwin ◽  
Marthie M. Grobler
Keyword(s):  
Data Fusion ◽  
South African ◽  
Cyber Security ◽  
National Level ◽  
Data Sources ◽  
Sensor Data ◽  
Fusion Model ◽  
System A ◽  
Attack Surface

The potential attack surface of a nation is large and no single source of cyber security data provides all the required information to accurately describe the cyber security readiness of a nation. There are a variety of specialised data sources available to assess the state of a nation in key areas such as botnets, spam servers and incorrectly configured hosts. By applying data fusion principles, the potential exists to provide a representative view of all combined data sources. This research will examine a variety of currently available Internet data sources and apply it to an adapted Joint Directors of Laboratories (JDL) data fusion model in order to illustrate the potential gains and current limitations. The JDL model has been adapted to suit national level cyber sensor data fusion with the aim to formally define and reduce data ambiguity and enhance fusion capability in a real world system. A case study highlights the results of applying available open source security information against the model to relate to the current South African cyber landscape.

Adaptation of the JDL Model for Multi-Sensor National Cyber Security Data Fusion

2019 ◽  
pp. 92-107
Author(s):  
Ignatius Swart ◽  
Barry V. W. Irwin ◽  
Marthie M. Grobler
Keyword(s):  
Data Fusion ◽  
South African ◽  
Cyber Security ◽  
National Level ◽  
Data Sources ◽  
Sensor Data ◽  
Fusion Model ◽  
System A ◽  
Attack Surface

The potential attack surface of a nation is large and no single source of cyber security data provides all the required information to accurately describe the cyber security readiness of a nation. There are a variety of specialised data sources available to assess the state of a nation in key areas such as botnets, spam servers and incorrectly configured hosts. By applying data fusion principles, the potential exists to provide a representative view of all combined data sources. This research will examine a variety of currently available Internet data sources and apply it to an adapted Joint Directors of Laboratories (JDL) data fusion model in order to illustrate the potential gains and current limitations. The JDL model has been adapted to suit national level cyber sensor data fusion with the aim to formally define and reduce data ambiguity and enhance fusion capability in a real world system. A case study highlights the results of applying available open source security information against the model to relate to the current South African cyber landscape.

Adaptation of the JDL Model for Multi-Sensor National Cyber Security Data Fusion

2019 ◽  
pp. 311-326
Author(s):  
Ignatius Swart ◽  
Barry V. W. Irwin ◽  
Marthie M. Grobler
Keyword(s):  
Data Fusion ◽  
South African ◽  
Cyber Security ◽  
National Level ◽  
Data Sources ◽  
Sensor Data ◽  
Fusion Model ◽  
System A ◽  
Attack Surface

The potential attack surface of a nation is large and no single source of cyber security data provides all the required information to accurately describe the cyber security readiness of a nation. There are a variety of specialised data sources available to assess the state of a nation in key areas such as botnets, spam servers and incorrectly configured hosts. By applying data fusion principles, the potential exists to provide a representative view of all combined data sources. This research will examine a variety of currently available Internet data sources and apply it to an adapted Joint Directors of Laboratories (JDL) data fusion model in order to illustrate the potential gains and current limitations. The JDL model has been adapted to suit national level cyber sensor data fusion with the aim to formally define and reduce data ambiguity and enhance fusion capability in a real world system. A case study highlights the results of applying available open source security information against the model to relate to the current South African cyber landscape.

scholarly journals Hybrid Particle Swarm Optimization for Multi-Sensor Data Fusion

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2792 ◽  
Author(s):  
Hyunseok Kim ◽  
Dongjun Suh
Keyword(s):  
Particle Swarm Optimization ◽  
Data Fusion ◽  
Large Scale ◽  
Synergistic Effects ◽  
Particle Swarm ◽  
Sensor Data ◽  
Fusion Model ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization

A hybrid particle swarm optimization (PSO), able to overcome the large-scale nonlinearity or heavily correlation in the data fusion model of multiple sensing information, is proposed in this paper. In recent smart convergence technology, multiple similar and/or dissimilar sensors are widely used to support precisely sensing information from different perspectives, and these are integrated with data fusion algorithms to get synergistic effects. However, the construction of the data fusion model is not trivial because of difficulties to meet under the restricted conditions of a multi-sensor system such as its limited options for deploying sensors and nonlinear characteristics, or correlation errors of multiple sensors. This paper presents a hybrid PSO to facilitate the construction of robust data fusion model based on neural network while ensuring the balance between exploration and exploitation. The performance of the proposed model was evaluated by benchmarks composed of representative datasets. The well-optimized data fusion model is expected to provide an enhancement in the synergistic accuracy.

Sensor Data Fusion for Location Awareness

2007 ◽  
pp. 863-869
Author(s):  
O. Sekkas ◽  
S. Hadjiefthymiades ◽  
E. Zervas
Keyword(s):  
Data Fusion ◽  
Bayesian Networks ◽  
Dynamic Bayesian Networks ◽  
Sensor Data ◽  
Rfid Tags ◽  
Location Awareness ◽  
The Past ◽  
Accuracy And Precision ◽  
Multiple Sensor ◽  
Sensor Information

During the past few years, several location systems have been proposed that use multiple technologies simultaneously in order to locate a user. One such system is described in this article. It relies on multiple sensor readings from Wi-Fi access points, IR beacons, RFID tags, and so forth to estimate the location of a user. This technique is known better as sensor information fusion, which aims to improve accuracy and precision by integrating heterogeneous sensor observations. The proposed location system uses a fusion engine that is based on dynamic Bayesian networks (DBNs), thus substantially improving the accuracy and precision.

Remaining useful life prediction based on a multi-sensor data fusion model

2021 ◽  
Vol 208 ◽  
pp. 107249
Author(s):  
Naipeng Li ◽  
Nagi Gebraeel ◽  
Yaguo Lei ◽  
Xiaolei Fang ◽  
Xiao Cai ◽  
...  
Keyword(s):  
Data Fusion ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Fusion Model ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Useful Life

Enhanced-resolution fluorescence lifetime imaging from multiple sensor data fusion

2020 ◽  
Author(s):  
Areeba Fatima ◽  
Jamie Whitelaw ◽  
Vytautas Zickus ◽  
Ewan McGhee ◽  
Robert Insall ◽  
...  
Keyword(s):  
Data Fusion ◽  
Fluorescence Lifetime ◽  
Sensor Data ◽  
Fluorescence Lifetime Imaging ◽  
Sensor Data Fusion ◽  
Lifetime Imaging ◽  
Enhanced Resolution ◽  
Multiple Sensor

scholarly journals Application of Multiple Sensor Data Fusion for the Analysis of Human Dynamic Behavior in Space: Assessment and Evaluation of Mobility-Related Functional Impairments

2017 ◽  
Vol 10 (04) ◽  
pp. 182-203
Author(s):  
Thompson Sarkodie-Gyan ◽  
Huiying Yu ◽  
Melaku Bogale ◽  
Noe Vargas Hernandez ◽  
Miguel Pirela-Cruz
Keyword(s):  
Data Fusion ◽  
Dynamic Behavior ◽  
Sensor Data ◽  
Functional Impairments ◽  
Assessment And Evaluation ◽  
Sensor Data Fusion ◽  
Multiple Sensor
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