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Multi-sensor data fusion combines data from multiple sensors to create more accurate datasets, with applications in meteorology, environmental analysis, transportation management, and target tracking. It relies on sensor technology, mathematical processes, and data integration, and mimics human perception. Low-level data fusion combines raw data to create more specific and synthetic datasets.
Multi-sensor data fusion is the process of acquiring multiple datasets from multiple sensors with the intent of creating a more accurate dataset. Often considered more accurate than single-sensor data, this type of information fusion has many applications. For example, combining data from a temperature sensor with a wind chill sensor can help someone inside understand how cold it might feel outside. In addition to meteorological applications, multi-sensor data analysis can also be applied to environmental analysis, transportation management and target tracking.
The many applications of multisensor data fusion show how useful information fusion can be. When data comes from multiple sources, specific datasets can be reviewed, replaced, or trimmed from the merged data. For example, a marine biologist interested in tracking whales could use data fusion to track factors he believes influence whale habits. The end result of multi-sensor data fusion processes could be a visual map of whale movement correlated to seawater temperature or other factors. These types of applications rely on many techniques, including physics equipment, algorithms, and the mathematics related to information fusion.
Sensor technology, mathematical processes, and the application of fused data sets determine the practical application of multi-sensor data fusion. The technology and processes used to combine the integrated data can be thought of as mimicking the natural human ability to perceive an environment and make decisions based on the five senses. However, the technology-based sensors and related techniques required for data fusion may be more specific than human perception.
The combination of these specific datasets is a defining feature of multisensor data fusion and differentiates information fusion from data integration. Data integration is a large part of the multi-sensor data fusion process, however, and could be considered a building block for creating more advanced datasets. For example, a sensor may record several sets of temperatures over a period of time and then later create a larger set over a longer period of time. This process differs from multi-sensor data analysis, however, in that it generally does not include information from many different sources.
As part of the data fusion process, data integration is inseparable. Without the insights provided by strong data integration, there would be no basis for multi-sensor data fusion. In fact, a common type of multi-sensor data analysis is low-level data fusion. This process refers to the combination of raw data to create new datasets which generally should be more specific and synthetic than the raw data.
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