An introduction to multisensor data fusion

Abstract

Multisensor data fusion is an emerging technology applied to Department of Defense (DoD) areas such as automated target recognition, battlefield surveillance, and guidance and control of autonomous vehicles, and to non-DoD applications such as monitoring of complex machinery, medical diagnosis, and smart buildings. Techniques for multisensor data fusion are drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and other areas. This paper provides a tutorial on data fusion, introducing data fusion applications, process models, and identification of applicable techniques. Comments are made on the state-of-the-art in data fusion.

Direct perception of global invariants is not a fruitful notion

“…Again, the solution may be task specific, with "input-driven" responses representing one end of a multidimensional continuum. This view is consistent with the engineering literature, which considers multisensory integration as a subproblem of "data fusion," in which any number of inputs, not just sensory, are combined to form a percept of an environment object or event (Hall & Llinas 1997).…”

An adaptive version of k-medoids to deal with the uncertainty in clustering heterogeneous data using an intermediary fusion approach

“…Data fusion approaches have become popular for heterogeneous data as they handle the process of integration of multiple data and knowledge from the same real-world object into a consistent, accurate, and useful representation. In practice, data fusion has been evolving for a long time in multi-sensor research (Hall and Llinas, 1997;Khaleghi et al, 2013) and other areas such as robotics and machine learning (Abidi and Gonzalez, 1992;Faouzi et al, 2011). However, there has been little interaction with data mining research until recently (Dasarathy, 2003).…”

Context-based Information Fusion: A survey and discussion

“…SA systems, for example, have to go through a number of processing steps, also combining heterogeneous data, in order to estimate the status and intentions (or purpose) of non-cooperative entities (or process/system) [98]. In addition, observations from sensors are generally noisy and sources of information can have different level of trust and provide outputs with different quality [135], therefore making fusion a real necessity [124].…”

“…In JDL model fusion terminology and according to the general acceptance of the term with the fusion community, the term ''high'' here refers to fusion levels above level 1. At these levels the fusion of data and information is largely (but not exclusively) conducted at the symbolic level [124].…”