Title :
Recursive algorithms for information fusion using belief functions with applications to target identification
Author_Institution :
Dept. of Electr. Eng., Wright State Univ., Dayton, OH, USA
Abstract :
Recursive centralized and distributed algorithms for fusing temporal and spatial information are developed for joint and disjoint measurement data structures using the belief function approach. The algorithms have strong capability for handling information uncertainty. The effectiveness of the algorithms is demonstrated by a target identification problem
Keywords :
pattern recognition; sensor fusion; belief function approach; belief functions; disjoint measurement data structures; distributed algorithms; information fusion; information uncertainty; joint measurement data structures; recursive centralized algorithms; spatial information; temporal information; Artificial intelligence; Bayesian methods; Cost function; Covariance matrix; Data structures; Filtering algorithms; Information filtering; Information filters; Kalman filters; Uncertainty;
Conference_Titel :
Control Applications, 1992., First IEEE Conference on
Conference_Location :
Dayton, OH
Print_ISBN :
0-7803-0047-5
DOI :
10.1109/CCA.1992.269727
