Network and infrastructure considerations for hard and soft information fusion processes

The changing landscape of defense and security applications, as well as existing needs in other domains, has given rise to the need to design and develop information fusion systems that entail the combination of hard (physical sensor) data and observations from humans (soft data) in a distributed networked environment ([1], [2]). Such fusion processes stress both traditional fusion-based algorithmic design strategies as well as imputing requirements onto the design of appropriate decentralized architectures. In designing a framework for such an environment, the system architecture must be considered both at the level of information infrastructure and at the network/software level of implementation. From the information infrastructure perspective, primary concerns include which observations will be fed into the system, the intended goals (e.g. data mining, hypothesis generation/testing), how humans interact with the system (e.g. information gathering, analysis, process evaluation/refinement), and which levels of state estimation and fusion are appropriate at a given stage of the process [3]. Bisantz et al [4] discuss aspects of how humans interact with such new distributed networked information fusion systems and identify "touch points" where innovations can support improved system performance. An effective network/software architecture is tightly intertwined with the information infrastructure. The former must meet the functional requirements of the latter without adding unnecessary complexity, prohibiting scalability or extensibility, putting undue performance limitations on the system, or compromising system security. Additionally, the system must have the agility and flexibility to successfully and rapidly complete complex tasks that were not possible to anticipate at design-time. This paper presents an information infrastructure for hard and soft fusion that balances the features offered by the current state of the art in computing paradigms (e.g. SOA, ESB) and data representation (e.g. RDF, OWL, TML, EML) with the requirements of our overarching concept of employment for hard and soft fusion capability. The paper provides guidance on determining the optimal software and network architecture design for enabling human-centric and distributed operations in time-critical, vastly heterogeneous, and highly unpredictable conditions. The designs described have been implemented using both synthetic data from our SYNCOIN data set ([5], [6]) and actual sensor/observational data collected at a Penn State test site.