Data fusion

The concept of data fusion has origins in the evolved capacity of humans and animals to incorporate information from multiple senses to improve their ability to survive.

The DFIG model (Level 0–5) explored the implications of situation awareness, user refinement, and mission management.

Each of these data sets exhibit a different spatial grid and sampling rate so a simple combination would likely create erroneous assumptions and taint the results of the analysis.

This enables scientists to identify key locations and times and form new insights into the interactions between the environment and animal behaviors.

The data from the different sensing technologies can be combined in intelligent ways to determine the traffic state accurately.

A Data fusion based approach that utilizes the road side collected acoustic, image and sensor data has been shown to combine the advantages of the different individual methods.

Using signal processing and data fusion techniques such as feature generation, feasibility study and principal component analysis (PCA) such sensory data will greatly improve the positive rate of classifying the motion and contextual relevant status of the device.

[10] Many context-enhanced information techniques are provided by Snidaro, et al.[11][12] Gaussian processes are a popular machine learning model.

[14] Recently, methods have been developed to enable efficient estimation within the resulting semiparametric model.