In the area of data fusion, dealing with heterogeneous data sources, numerous models have been proposed in last three decades to facilitate different application domains i.e. Department of Defense (DoD), monitoring of complex machinery, medical diagnosis and smart buildings. All of these models shar...
In the area of data fusion, dealing with heterogeneous data sources, numerous models have been proposed in last three decades to facilitate different application domains i.e. Department of Defense (DoD), monitoring of complex machinery, medical diagnosis and smart buildings. All of these models shared the theme of multiple levels processing to get more reliable and accurate information. In this paper, we consider five most widely acceptable fusion models (Intelligence Cycle, Joint Directors of Laboratories, Boyd control, Waterfall, Omnibus) applied to different areas for data fusion. When they are exposed to a real scenario, where large dataset from heterogeneous sources is utilize for object monitoring, then it may leads us to non-efficient and unreliable information for decision making. The proposed variation works better in terms of time and accuracy due to prior data diminution.
In the area of data fusion, dealing with heterogeneous data sources, numerous models have been proposed in last three decades to facilitate different application domains i.e. Department of Defense (DoD), monitoring of complex machinery, medical diagnosis and smart buildings. All of these models shared the theme of multiple levels processing to get more reliable and accurate information. In this paper, we consider five most widely acceptable fusion models (Intelligence Cycle, Joint Directors of Laboratories, Boyd control, Waterfall, Omnibus) applied to different areas for data fusion. When they are exposed to a real scenario, where large dataset from heterogeneous sources is utilize for object monitoring, then it may leads us to non-efficient and unreliable information for decision making. The proposed variation works better in terms of time and accuracy due to prior data diminution.
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제안 방법
In this paper, we have considered five different data fusion models to analyze the feasibility of our proposed model. In this model we have incorporated the locality mechanism in the whole fusion model as shown in Figure 1.
Due to extensive usability of the fusion models in commercial and industrial problems, it is necessary to define a model which can be easily adoptable to specific area without compromising on performance and effectiveness. In this paper, we have devised a mechanism at lower level of data fusion model through which we can attain higher efficiency by reducing the complexity of the raw data. In this model we have retained all the strengths of the Waterfall and Boyd Loop models.
In this paper, we have devised a variation to few well known existing fusion models for improvement with respect to time and efficiency. The beauty of this suggestion is that it can be incorporated to any existing fusion model without harming its effectiveness.
Information fusion took place in four stages using this model. The objective of the observation stage is used to gather required data from the environments based on the heterogeneous sensors and sources. Once data is collected, it relates the data with the current scenario, in other words its functionality is similar to JDL model’s level 2 processing.
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