Generalized Murty's algorithm with application to multiple hypothesis tracking

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 multi-frame 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 k- best multiple-frame association hypothesis selection capability, which is used for output ambiguity assessment, hypothesis space pruning, and multi-modal track outputs.