The Bin-Occupancy Filter and Its Connection to the PHD Filters

An algorithm that is capable not only of tracking multiple targets but also of ldquotrack managementrdquo-meaning that it does not need to know the number of targets as a user input-is of considerable interest. In this paper we devise a recursive track-managed filter via a quantized state-space (ldq...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2009-11, Vol.57 (11), p.4232-4246
Main Authors: Erdinc, O., Willett, P., Bar-Shalom, Y.
Format: Article
Language:English
Subjects:
Appeals
Applied sciences
Bins
cardinalized
CPHD
Density
Density measurement
Detection, estimation, filtering, equalization, prediction
Discretization
Equations
Exact sciences and technology
Filtering
Filters
Information, signal and communications theory
Mathematical analysis
Miscellaneous
multitarget tracking (MTT)
Probability density function
probability hypothesis density (PHD)
Probability hypothesis density filter
Recursive
Signal and communications theory
Signal processing
Signal, noise
State-space methods
Studies
Surveillance
Target tracking
Telecommunications and information theory
Time measurement
Tracking
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Summary:An algorithm that is capable not only of tracking multiple targets but also of ldquotrack managementrdquo-meaning that it does not need to know the number of targets as a user input-is of considerable interest. In this paper we devise a recursive track-managed filter via a quantized state-space (ldquobinrdquo) model. In the limit, as the discretization implied by the bins becomes as refined as possible (infinitesimal bins) we find that the filter equations are identical to Mahler's probability hypothesis density (PHD) filter, a novel track-managed filtering scheme that is attracting increasing attention. Thus, one contribution of this paper is an interpretation of, if not the PHD itself, at least what the PHD is doing. This does offer some intuitive appeal, but has some practical use as well: with this model it is possible to identify the PHD's ldquotarget-deathrdquo problem, and also the statistical inference structures of the PHD filters. To obviate the target death problem, PHD originator Mahler developed a new ldquocardinalizedrdquo version of PHD (CPHD). The second contribution of this paper is to extend the ldquobin-occupancyrdquo model such that the resulting recursive filter is identical to the cardinalized PHD filter.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2009.2025816