"Spooky Action at a Distance" in the Cardinalized Probability Hypothesis Density Filter

The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for estimating multiple target states with varying target number in clutter. In the present work, it is shown that a missed detection in one part of the field of view has a significant effect on the proba...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems Vol. 45; no. 4; pp. 1657 - 1664
Main Authors: Franken, D., Schmidt, M., Ulmke, M.
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2009
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Aircraft components
Algorithms
Bayesian methods
Clutter
Density
Electronic systems
Equations
Estimates
Mathematical analysis
Nonlinear filters
Object detection
Quantum entanglement
Recursive
Recursive estimation
State estimation
Target tracking
ISSN:0018-9251, 1557-9603
Online Access:Get full text
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Summary:The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for estimating multiple target states with varying target number in clutter. In the present work, it is shown that a missed detection in one part of the field of view has a significant effect on the probability hypothesis density (PHD) arbitrarily far apart from the missed detection. In the case of zero false alarm rate, this effect is particularly pronounced and can be calculated by solving the CPHD filter equations analytically. While the CPHD filter update of the total cardinality distribution is exact, the local target number estimate close to the missed detection is artificially strongly reduced. A first ad-hoc approach towards a "locally" CPHD filter for reducing this deficiency is presented and discussed.
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ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2009.5310327