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Title: Data association for the PHD filter
Author: Clark, DE, Bell, J
Year: 2005
Book / Journal: Proceedings of the 2005 Intelligent Sensors, Sensor Networks \& Information Processing Conference
Pages: 217-222
Abstract: The Probability Hypothesis Density (PHD) filter was developed as a suboptimal method for tracking a time varying number of targets. The first order statistical moment of the multiple target posterior distribution, called the Probability Hypothesis Density, gives the expected locations of the targets. This property is used instead of the full multi-target posterior distribution as it requires significantly less computation. Particle filter implementations have demonstrated the potential of the algorithm for real-time tracking applications. One of the main criticisms of the PHD filter is that there is no means of associating the same target between frames. Whilst this may be of advantage if the main concern is where the targets are, it is a major drawback if it is necessary to identify the trajectories of the different targets. Novel techniques for solving the problem of track continuity are presented here and demonstrated on simulated data.
Keywords: ,
BibTex: @inproceedings{ ISI:000235261200036,
Author = {Clark, DE and Bell, J},
Book-Group-Author = {{IEEE}},
Title = {{Data association for the PHD filter}},
Booktitle = {{Proceedings of the 2005 Intelligent Sensors, Sensor Networks \&
   Information Processing Conference}},
Year = {{2005}},
Pages = {{217-222}},
Note = {{Intelligent Sensors, Sensor Networks and Information Processing
   Conference, Melbourne, AUSTRALIA, DEC 05-08, 2005}},
Abstract = {{The Probability Hypothesis Density (PHD) filter was developed as a
   suboptimal method for tracking a time varying number of targets. The
   first order statistical moment of the multiple target posterior
   distribution, called the Probability Hypothesis Density, gives the
   expected locations of the targets. This property is used instead of the
   full multi-target posterior distribution as it requires significantly
   less computation. Particle filter implementations have demonstrated the
   potential of the algorithm for real-time tracking applications.
   One of the main criticisms of the PHD filter is that there is no means
   of associating the same target between frames. Whilst this may be of
   advantage if the main concern is where the targets are, it is a major
   drawback if it is necessary to identify the trajectories of the
   different targets. Novel techniques for solving the problem of track
   continuity are presented here and demonstrated on simulated data.}},
ISBN = {{0-7803-9399-6}},
Unique-ID = {{ISI:000235261200036}},

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