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...

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Vydané v:2007 10th International Conference on Information Fusion : Québec, Canada, 9-12 July 2007 s. 1 - 8
Hlavní autori: Fortunato, E., Kreamer, W., Mori, S., Chee-Yee Chong, Castanon, G.
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.07.2007
Predmet:
All Source Track and Identify Fuser (ATIF)
data association hypothesis evaluation
Engines
Forward contracts
Fusion power generation
Generalized Murty's algorithm
Information technology
Integer linear programming
k-best multiple frame assignment (MFA)
Lagrangian functions
Linear programming
Multidimensional systems
multiple target tracking (MHT)
Relaxation methods
Target tracking
track-oriented MHT
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Shrnutí: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.
DOI:10.1109/ICIF.2007.4408017