Sensor fusion

Sensor fusion is a process of combining <a href="/facts/Sensor/RN8SXbjs">sensor</a> data or data derived from disparate sources so that the resulting <a href="/facts/Information/3LdrzXtJ">information</a> has less uncertainty than would be possible if these sources were used individually. For instance, one could potentially obtain a more accurate location estimate of an indoor object by combining multiple data sources such as video cameras and <a href="/facts/WiFi_localization/n3ILE99s">WiFi localization</a> signals. The term uncertainty reduction in this case can mean more accurate, more complete, or more dependable, or refer to the result of an emerging view, such as <a href="/facts/Stereoscopy/jfHumNV8">stereoscopic</a> 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 <a href="/facts/Homogeneity_and_heterogeneity/y84KQJxl">heterogeneous</a> or homogeneous sensors, <a href="/facts/Soft_sensor/HWeqtwmM">soft sensors</a>, and history values of sensor data, while indirect fusion uses information sources like <a href="/facts/A_priori_and_a_posteriori/C567SkcV">a priori</a> knowledge about the environment and human input.
Sensor fusion is also known as (multi-sensor) <a href="/facts/Data_fusion/71rNwLLs">data fusion</a> and is a subset of <a href="/facts/Information_integration/veGbc8F7">information fusion</a>.

Sensor fusion open-in-new

Sensor fusion