Generalized Murty's algorithm with application to multiple hypothesis tracking

Evan Fortunato, William Kreamer, Shozo Mori, Chee-Yee Chong, Gregory Castanon BAE Systems, Advanced Information Technologies, Burlington, MA, U.S.A. {evan.fortunato,bill.kreamer,shozo.mori,chee.chong,greg.castanon}@baesystems.com This work supported by DARPA/IXO and AFRL/IFKE under Contract No. FA8750-05-C-0115 Approved for public release; distribution is unlimited Abstract – This paper describes a generalization of Murty’s algorithm generating ranked solutions for classical assignment problems. The generalization extends the domain to a general class of zero-one integer linear programming problems that can be used to solve multiframe data association problems for track-oriented multiple hypothesis tracking (MHT). The generalized Murty’s algorithm mostly follows the steps of Murty’s ranking algorithm for assignment problems. It was implemented in a hybrid data fusion engine, called AllSource Track and Identity Fusion (ATIF), to provide a kbest multiple-frame association hypothesis selection capability, which is used for output ambiguity assessment, hypothesis space pruning, and multi-modal track outputs.