Fusion of disparate identity estimates for shared situation awareness in a network-centric environment q Martin Oxenham a, * , Subhash Challa b,1 , Mark Morelande c,1 a Intelligence Surveillance and Reconnaissance Division, Defence Science and Technology Organisation (DSTO), Edinburgh 5111, South Australia, Australia b Computer Systems Engineering, Faculty of Engineering, The University of Technology Sydney, Broadway, New South Wales 2007, Australia c Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, Victoria 3010, Australia Received 6 December 2004; received in revised form 9 September 2005; accepted 12 September 2005 Available online 2 November 2005 Abstract Network-centric warfare (NCW) and the interoperability of joint and coalition forces are among the future warfighting concepts iden- tified 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 dis- parate 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 con- sidered 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. (Ó Commonwealth of Australia 2005.) Ó 2005 Elsevier B.V. All rights reserved. Keywords: Target identification; Decentralised data fusion; Network-centric warfare; Interoperability; Disparate uncertainty 1. Introduction Network-centric warfare (NCW) and the interoperabil- ity of joint and coalition forces are among the future war- fighting concepts that have been identified by defence. 2 The concept of NCW refers to the ‘‘linking of sensors, engage- ment systems and decision-makers into an effective and responsive whole’’ and is achieved through ‘‘shared situation awareness, clear procedures and the information connectivity needed to synchronise the actions of the defence force to meet the commanderÕs intent [2]’’. Military interoperability on the other hand refers to ‘‘the ability of systems, units or forces to provide services to and to accept services from other systems, units or forces and to use the services so exchanged to enable them to operate effectively together [3]’’. While the two concepts are distinct, it is evi- dent that NCW relies heavily on interoperability. From the perspective of shared situation awareness, data fusion has much to offer both NCW and military interoperability. It has long been acknowledged that dis- tributed data fusion is a key enabling technology for NCW, especially in terms of distributed tracking and iden- tification, and more recently through the development of distributed agents, ontologies and formal theories. For dis- cussions of these topics, see for example [4–21]. 1566-2535/$ - see front matter Ó 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.inffus.2005.09.003 q A short version of this study was presented at the Seventh Interna- tional Conference on Information Fusion (FUSION 2004) in Stockholm, Sweden [1]. * Corresponding author. Tel.: +61 8 8259 6974; fax: +61 8 8259 6673. E-mail address: martin.oxenham@dsto.defence.gov.au (M. Oxenham). 1 Supported by the DSTO Tracking and Data Fusion Laboratory. 2 The remaining concept, which is not considered in this paper, is that of effects-based operations. See [2] for details. www.elsevier.com/locate/inffus Information Fusion 7 (2006) 395–417