Data fusion of multiple-sensors attribute information for target-identity estimation using a Dempster-Shafer evidential combination algorithm

摘要

The research and development group at Loral Canada is in the second phase in the development of a data fusion demonstration model (DFDM) for a naval anti-air warfare platform to be used as a workbench tool to perform exploratory research. The software has been designed to be implemented within the software environment of the Canadian Patrol Frigate (CPF). The second version of DFDM has the capability to fuse data from the following CPF sensors: surveillance radars, electronics support measure, identification friend or foe, communication intercept operator and a tactical data link. During the first phase, the project has demonstrated the feasibility of fusing the sensor attribute information using a modified version of the Dempster-Shafer evidential combination algorithm. A significant enhancement has been the addition of pruning rules to reduce the set of identity propositions which otherwise would be too large to comply with the DFDM real- time requirements. Another improvement has been the use of fuzzy logic to make possible the fusion of apparently incomplete attribute information coming from different sensors. This paper describes the main features of the evidential combination algorithm that we have implemented in the DFDM system. A benchmark scenario has been selected to quantitatively demonstrate the capability of the attribute fusion algorithm.