Generalized Murty's algorithm with application to multiple hypothesis tracking

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 All- Source 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.