Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter

The probability hypothesis density (PHD) recursion propagates the posterior intensity of the random finite set (RFS) of targets in time. The cardinalized PHD (CPHD) recursion is a generalization of the PHD recursion, which jointly propagates the posterior intensity and the posterior cardinality dist...

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Vydané v:	IEEE transactions on signal processing Ročník 55; číslo 7; s. 3553 - 3567
Hlavní autori:	Ba-Tuong Vo, Ba-Ngu Vo, Cantoni, A.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.07.2007 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Applied sciences Australia Bayesian methods Cardinalized probability hypothesis density (CPHD) filter Closed-form solution Complexity Density Detection, estimation, filtering, equalization, prediction Exact sciences and technology Exact solutions Filtering Filters Information, signal and communications theory Mathematical analysis Mathematical models multitarget Bayesian filtering multitarget tracking probability hypothesis density (PHD) filter Radar tracking random finite sets (RFSs) Recursion Signal and communications theory Signal, noise State estimation Studies Target tracking Telecommunications and information theory Uncertainty Performance evaluation Target tracking Filtering Probabilistic approach Terms-Cardinalized probability hypothesis density (CPHD) filter probability hypothesis density (PHD) filter Multiple target multitarget Bayesian filtering Algorithm Implementation random finite sets (RFSs) Probability density Accuracy Simulation Non linear model Random set State estimation multitarget tracking Posterior distribution Linearization
ISSN:	1053-587X, 1941-0476
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Shrnutí:	The probability hypothesis density (PHD) recursion propagates the posterior intensity of the random finite set (RFS) of targets in time. The cardinalized PHD (CPHD) recursion is a generalization of the PHD recursion, which jointly propagates the posterior intensity and the posterior cardinality distribution. In general, the CPHD recursion is computationally intractable. This paper proposes a closed-form solution to the CPHD recursion under linear Gaussian assumptions on the target dynamics and birth process. Based on this solution, an effective multitarget tracking algorithm is developed. Extensions of the proposed closed-form recursion to accommodate nonlinear models are also given using linearization and unscented transform techniques. The proposed CPHD implementations not only sidestep the need to perform data association found in traditional methods, but also dramatically improve the accuracy of individual state estimates as well as the variance of the estimated number of targets when compared to the standard PHD filter. Our implementations only have a cubic complexity, but simulations suggest favorable performance compared to the standard Joint Probabilistic Data Association (JPDA) filter which has a nonpolynomial complexity.
Bibliografia:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23
ISSN:	1053-587X 1941-0476
DOI:	10.1109/TSP.2007.894241