Data fusion

Data Fusion is a process that combines multiple data sources to identify more accurate information than that available from each source alone. It uses data from different sources — including sensors, local files, databases, and external sources — to create a unified model of a given system, situation, concept, or scene. This allows for a more comprehensive understanding than any of the sources could provide on their own.

Data Fusion can be used as part of a data mining process, enhancing the accuracy of the results when mining a large-scale dataset. It can also be used in computer vision for image feature extraction, such as detecting objects or interpreting a scene.

In addition to providing improved accuracy, Data Fusion helps provide more consistent data. By merging multiple sources, the likelihood of incorrect data is decreased, as inconsistencies are identified and corrected. This results in a higher-quality set of results.

Data Fusion is essential in many areas of computing, such as robotics, artificial intelligence, security, and the Internet of Things. By creating unified models of situations, organizations can optimize decisions and achieve higher levels of accuracy.

Data Fusion can be performed using various techniques, such as Bayesian inference, Kalman Filtering, Dempster-Shafer Theory, or Fuzzy Logic. The appropriate technique depends on the data sources and the objectives of the process.

Data Fusion is an important tool for improving the accuracy and consistency of data sets. It is a powerful technique that can enhance the understanding of a given system, situation, concept, or scene.