Fusion of disparate identity estimates for shared situation awareness in a network-centric environment

Network-centric warfare (NCW) and the interoperability of joint and coalition forces are among the future warfighting concepts identified by defence. To realise the goals of interoperability and shared situation awareness for NCW, it has long been acknowledged that data fusion is a key enabling technology. Typically, however, distributed data fusion, which is relevant to NCW, and the fusion of disparate types of uncertain data, which is relevant to interoperability, have been investigated separately. Ideally, for shared situation awareness, the system should be capable of performing both aspects of data fusion. In this paper, these facets of data fusion are considered in unison for the automatic target identification problem. In particular, novel Bayesian and generalised Bayesian algorithms are formulated for fusing estimates of target identity generated by local heterogeneous data fusion systems in a network, each of which expresses target identity estimates as either finite probability distributions or Dempster–Shafer belief functions. An example drawn from the literature is used to illustrate the algorithms and their relative performances are assessed in the context of the example to identify issues of possible relevance to distributed target identification in a more general setting. (© Commonwealth of Australia 2005.)