NDLI: Accurate Murty's algorithm for multitarget top hypothesis extraction

Content Provider	IEEE Xplore Digital Library
Author	Xiaofan He Tharmarasa, R. Pelletier, M. Kirubarajan, T.
Copyright Year	2011
Description	Author affiliation: Dept. of ECE, McMaster University, Hamilton, Canada (Xiaofan He; Tharmarasa, R.; Kirubarajan, T.) \|\| ICx Radar Systems, Laval, Canada (Pelletier, M.)
Abstract	In most hypothesis-oriented Multiple Hypothesis Tracking (MHT) implementations, the target-to-measurement data association is typically solved by using the Murty's algorithm. However, the Murty's algorithm has no control over the diversity of target-to-measurement associations — often the top associations vary only slightly. In addition, in practical tracking solutions, tracks are often grouped as tentative or continued. It was observed with real data sets that in the associations, the top hypotheses consist of mostly similar associations with the same confirmed tracks along with some permutations of new measurements. The result is that a fixed set of confirmed tracks dominate diversity of the association tree. To overcome this problem, a modified Murty's algorithm, which can achieve any user defined (or adaptable) diversity of track-to-measurement association of different types of tracks, is proposed in this paper. Numerical examples are provided to demonstrate the improved efficiency in hypotheses generation by the proposed method.
Starting Page	1
Ending Page	8
File Size	282743
Page Count	8
File Format	PDF
ISBN	9781457702679
e-ISBN	9780982443828
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Intl Society of Info Fusion
Subject Keyword	Target tracking Nickel Algorithm design and analysis Partitioning algorithms Radar tracking Clutter Current measurement data association K-best assignment Murty's algorithm
Content Type	Text
Resource Type	Article

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