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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Vydáno v:IEEE transactions on signal processing Ročník 55; číslo 7; s. 3553 - 3567
Hlavní autoři: Ba-Tuong Vo, Ba-Ngu Vo, Cantoni, A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY IEEE 01.07.2007
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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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Popis
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.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2007.894241