- Sensor fusion
Sensor fusion is the combining of
sensory data or data derived from sensory data from disparate sources such that the resulting information is in some sense "better" than would be possible when these sources were used individually. The term "better" in that case can mean more accurate, more complete, or more dependable, or refer to the result of an emerging view, such as stereoscopic vision (calculation of depth information by combining two-dimensional images from two cameras at slightly different viewpoints).
The data sources for a fusion process are not specified to originate from identical sensors. One can distinguish "direct fusion", "indirect fusion" and fusion of the outputs of the former two. Direct fusion is the fusion of sensor data from a set of
heterogeneousor 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". Transducer Markup Language(TML) is an XML based markup language which enables sensor fusion.
Examples of sensors
ensor fusion algorithms
Sensor fusion is a term that covers a number of methods and algorithms, including:
There are several categories or levels of sensor fusion that are commonly used.
* Level 0 - Data Alignment
* Level 1 - Entity Assessment (e.g. signal/feature/object)
** Tracking and object detection/recognition/identification
* Level 2 - Situation Assessment
* Level 3 - Impact Assessment
* Level 4 - Process Refinement (i.e. sensor management)
* Level 5 - User Refinement
[http://www.infofusion.buffalo.edu/tm/Dr.Llinas'stuff/Rethinking%20JDL%20Data%20Fusion%20Levels_BowmanSteinberg.pdf Rethinking JDL Data Fusion Levels]
Fisher's methodfor combining independent tests of significance
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