In a typical consensus scenario, a group of adaptive agents (i.e., sensors, fusion nodes, people, etc.) iteratively interact with their neighbors and update their (current) states or opinions toward collectively estimating some phenomenon of interest without any global coordination. Understanding the dynamics of consensus formation in such settings, where the collective behavior is governed solely by the local interactions, is a topic of overlapping interest for researchers in multiple domains [1-19]. With the emergence of social networks as a dominant force in modern society, the importance of understanding the mathematical underpinnings that may lead to a consensus in such complex fusion environments  is becoming more and more important. However, in addition to agent interactions being ad hoc and/or dynamic with possibly asynchronous communications (i.e., delays), sensing in such environments are often distributed and involves a large numbers of heterogeneous sources that include a mix of both soft (i.e., human or human-based) and hard (i.e., conventional physics-based) sensors. Therefore, convergence analysis in such complex fusion networks is a challenging task due mainly to the unstructured environment and the difficulties associated with adequately modeling the agent opinions itself.
|Original language||English (US)|
|Title of host publication||Multisensor Data Fusion|
|Subtitle of host publication||From Algorithms and Architectural Design to Applications|
|Number of pages||19|
|State||Published - Jan 1 2017|
ASJC Scopus subject areas
- Physics and Astronomy(all)