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Data fusion is the process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source. Data fusion processes are often categorized as low, intermediate, or high, depending on the processing stage at which fusion takes place. [ 1 ]
Direct fusion is the fusion of sensor data from a set of heterogeneous or homogeneous sensors, soft sensors, and history values of sensor data, while indirect fusion uses information sources like a priori knowledge about the environment and human input. Sensor fusion is also known as (multi-sensor) data fusion and is a subset of information fusion.
Hank Asher (May 9, 1951 – January 11, 2013) [1] was an American businessman who founded several data fusion and data mining companies that compile information about companies, individuals and their interrelationships from thousands of different electronic databases. [2] [3] He was known by industry insiders as "the father of data fusion." [4] [5]
As such, it is a common sensor fusion and data fusion algorithm. Noisy sensor data, approximations in the equations that describe the system evolution, and external factors that are not accounted for, all limit how well it is possible to determine the system's state. The Kalman filter deals effectively with the uncertainty due to noisy sensor ...
Information fusion, which is a related term, involves the combination of information into a new set of information towards reducing redundancy and uncertainty. [ 1 ] Examples of technologies available to integrate information include deduplication , and string metrics which allow the detection of similar text in different data sources by fuzzy ...
Data augmentation is a statistical technique which allows maximum likelihood estimation from incomplete data. [1] [2] Data augmentation has important applications in Bayesian analysis, [3] and the technique is widely used in machine learning to reduce overfitting when training machine learning models, [4] achieved by training models on several slightly-modified copies of existing data.
Data integration refers to the process of combining, sharing, or synchronizing data from multiple sources to provide users with a unified view. [1] There are a wide range of possible applications for data integration, from commercial (such as when a business merges multiple databases) to scientific (combining research data from different bioinformatics repositories).
The purpose of image fusion is not only to reduce the amount of data but also to construct images that are more appropriate and understandable for the human and machine perception. [1] [2] In computer vision, multisensor image fusion is the process of combining relevant information from two or more images into a single image. [3]