Sensor fusion

Sensor fusion is a process of combining sensor data or data derived from disparate sources so that the resulting information 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 WiFi localization 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 stereoscopic vision (calculation of depth information by combining two-dimensional images from two cameras at slightly different viewpoints).

[1][2] The data sources for a fusion process are not specified to originate from identical sensors.

denote two estimates from two independent sensor measurements, with noise variances

is to apply inverse-variance weighting, which is also employed within the Fraser-Potter fixed-interval smoother, namely [6] where

It can be seen that the fused result is simply a linear combination of the two measurements weighted by their respective information.

[7] Another (equivalent) method to fuse two measurements is to use the optimal Kalman filter.

By applying Cramer's rule within the gain calculation it can be found that the filter gain is given by:[citation needed] By inspection, when the first measurement is noise free, the filter ignores the second measurement and vice versa.

"In this case, every sensor or platform can be viewed as an intelligent asset having some degree of autonomy in decision-making.

[9][10] These mechanisms provide a way to resolve conflicts or disagreements and to allow the development of dynamic sensing strategies.

Sensors are in redundant (or competitive) configuration if each node delivers independent measures of the same properties.

This configuration can be used in error correction when comparing information from multiple nodes.

Redundant strategies are often used with high level fusions in voting procedures.

This strategy is used for fusing information at raw data level within decision-making algorithms.

Complementary features are typically applied in motion recognition tasks with neural network,[13][14] hidden Markov model,[15][16] support vector machine,[17] clustering methods and other techniques.

Cooperative sensor strategy gives information impossible to obtain from single nodes.

[28] More precisely, sensor fusion can be performed fusing raw data coming from different sources, extrapolated features or even decision made by single nodes.

[35] Although technically not a dedicated sensor fusion method, modern convolutional neural network based methods can simultaneously process many channels of sensor data (such as hyperspectral imaging with hundreds of bands [36]) and fuse relevant information to produce classification results.