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Sequential Monte Carlo methods for multitarget filtering with random finite sets
 of F(S) by PX(T) � P(X ∈ T). However, RST is
, 2005
"... Abstract — Random finite sets are natural representations of multitarget states and observations that allow multisensor multitarget filtering to fit in the unifying random set framework for Data Fusion. Although the foundation has been established in the form of Finite Set Statistics (FISST), its ..."
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Cited by 103 (15 self)
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Abstract — Random finite sets are natural representations of multitarget states and observations that allow multisensor multitarget filtering to fit in the unifying random set framework for Data Fusion. Although the foundation has been established in the form of Finite Set Statistics (FISST), its relationship to conventional probability is not clear. Furthermore, optimal Bayesian multitarget filtering is not yet practical due to the inherent computational hurdle. Even the Probability Hypothesis Density (PHD) filter, which propagates only the first moment (or PHD) instead of the full multitarget posterior, still involves multiple integrals with no closed forms in general. This article establishes the relationship between FISST and conventional probability that leads to the development of a sequential Monte Carlo (SMC) multitarget filter. In addition, a SMC implementation of the PHD filter is proposed and demonstrated on a number of simulated scenarios. Both of the proposed filters are suitable for problems involving nonlinear nonGaussian dynamics. Convergence results for these filters are also established.
B.N.: Data association and track management for the gaussian mixture probability hypothesis density filter
 IEEE Trans. Aerosp. Electron. Syst
, 2009
"... The Gaussian Mixture Probability Hypothesis Density (GMPHD) recursion is a closedform solution to the probability hypothesis density (PHD) recursion, which was proposed for jointly estimating the timevarying number of targets and their states from a sequence of noisy measurement sets in the prese ..."
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Cited by 15 (2 self)
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The Gaussian Mixture Probability Hypothesis Density (GMPHD) recursion is a closedform solution to the probability hypothesis density (PHD) recursion, which was proposed for jointly estimating the timevarying number of targets and their states from a sequence of noisy measurement sets in the presence of data association uncertainty, clutter and missdetection. However the GMPHD filter does not provide identities of individual target state estimates, that are needed to construct tracks of individual targets. In this paper, we propose a new multitarget tracker based on the GMPHD filter, which gives the association amongst state estimates of targets over time and provides track labels. Various issues regarding initiating, propagating and terminating tracks are discussed. Furthermore, we also propose a technique for resolving identities of targets in close proximity, which the PHD filter is unable to do on its own.
RaoBlackwellised Particle Filtering in Random Set Multitarget Tracking
, 2006
"... This article introduces a RaoBlackwellised particle filtering (RBPF) approach in the finite set statistics (FISST) multitarget tracking framework. The RBPF approach is proposed in such a case, where each sensor is assumed to produce a sequence of detection reports each containing either one single ..."
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Cited by 9 (0 self)
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This article introduces a RaoBlackwellised particle filtering (RBPF) approach in the finite set statistics (FISST) multitarget tracking framework. The RBPF approach is proposed in such a case, where each sensor is assumed to produce a sequence of detection reports each containing either one singletarget measurement, or a "no detection" report. The tests cover two di#erent measurement models: a linearGaussian measurement model, and a nonlinear model linearised in the extended Kalman filter (EKF) scheme. In the tests, RaoBlackwellisation resulted in a significant reduction of the errors of the FISST estimators when compared to a previously proposed direct particle implementation. In addition, the RBPF approach was shown to be applicable in nonlinear bearingsonly multitarget tracking.
Neighbor discovery in a wireless sensor network: Multipacket reception capability and physicallayer signal processing
 in Proc. 48th Annu. Allerton Conf. Commun., Control, Comput
"... ar ..."
Random set particle filter for bearingsonly multitarget tracking
 Proceedings of SPIE
, 2005
"... The random set approach to multitarget tracking is a theoretically sound framework that covers joint estimation of the number of targets and the state of the targets. This paper describes a particle filter implementation of the random set multitarget filter. The contribution of this paper to the ran ..."
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Cited by 2 (0 self)
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The random set approach to multitarget tracking is a theoretically sound framework that covers joint estimation of the number of targets and the state of the targets. This paper describes a particle filter implementation of the random set multitarget filter. The contribution of this paper to the random set tracking framework is the formulation of a measurement model where each sensor report is assumed to contain at most one measurement. The implemented filter was tested in synthetic bearingsonly tracking scenarios containing up to two targets in the presence of false alarms and missed measurements. The estimated target state consisted of 2D position and velocity components. The filter was capable to track the targets fairly well despite of the missing measurements and the relatively high false alarm rates. In addition, the filter showed robustness against wrong parameter values of false alarm rates. The results that were obtained during the limited tests of the filter show that the random set framework has potential for challenging tracking situations. On the other hand, the computational burden of the described implementation is quite high and increases approximately linearly with respect to the expected number of targets.
Multiuser detection in a dynamic environment
"... Abstract — In mobile multipleaccess communications, not only the location of active users, but also their number varies with time. In typical analyses, multiuser detection theory is developed under the assumption that the number of active users is constant and known at the receiver, and coincides w ..."
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Abstract — In mobile multipleaccess communications, not only the location of active users, but also their number varies with time. In typical analyses, multiuser detection theory is developed under the assumption that the number of active users is constant and known at the receiver, and coincides with the maximum number of users entitled to access the system. This assumption is often overly pessimistic, since many users might be inactive at any given time, and detection under the assumption of a number of users larger than the real one may impair performance. This paper undertakes a different, more general approach to the problem of identifying active users and estimating their parameters and data in a dynamic environment where users are continuously entering and leaving the system. Using a mathematical tool known as Random Set Theory, we derive Bayesianfilter equations which describe the evolution with time of the a posteriori probability density of the unknown user parameters, and use this density to derive optimum detectors. I.
r Human Brain Mapping 30:1911–1921 (2009) r Dynamical MEG Source Modeling With MultiTarget Bayesian Filtering
"... r r Abstract: We present a Bayesian filtering approach for automatic estimation of dynamical source models from magnetoencephalographic data. We apply multitarget Bayesian filtering and the theory of Random Finite Sets in an algorithm that recovers the life times, locations and strengths of a set ..."
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r r Abstract: We present a Bayesian filtering approach for automatic estimation of dynamical source models from magnetoencephalographic data. We apply multitarget Bayesian filtering and the theory of Random Finite Sets in an algorithm that recovers the life times, locations and strengths of a set of dipolar sources. The reconstructed dipoles are clustered in time and space to associate them with sources. We applied this new method to synthetic data sets and show here that it is able to automatically estimate the source structure in most cases more accurately than either traditional multidipole modeling or minimum current estimation performed by uninformed human operators. We also show that from real somatosensory evoked fields the method reconstructs a source constellation comparable to that obtained by multidipole modeling. Hum Brain Mapp 30:1911–1921, 2009. VC 2009 WileyLiss, Inc.
r Human Brain Mapping 000:000–000 (2009) r Dynamical MEG Source Modeling with MultiTarget Bayesian Filtering
"... r r Abstract: We present a Bayesian filtering approach for automatic estimation of dynamical source models from magnetoencephalographic data. We apply multitarget Bayesian filtering and the theory of Random Finite Sets in an algorithm that recovers the life times, locations and strengths of a set ..."
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r r Abstract: We present a Bayesian filtering approach for automatic estimation of dynamical source models from magnetoencephalographic data. We apply multitarget Bayesian filtering and the theory of Random Finite Sets in an algorithm that recovers the life times, locations and strengths of a set of dipolar sources. The reconstructed dipoles are clustered in time and space to associate them with sources. We applied this new method to synthetic data sets and show here that it is able to automatically estimate the source structure in most cases more accurately than either traditional multidipole modeling or minimum current estimation performed by uninformed human operators. We also show that from real somatosensory evoked fields the method reconstructs a source constellation comparable to that obtained by multidipole modeling. Hum Brain Mapp 00:000–000, 2009. VC 2009 WileyLiss, Inc.
Sequential Monte Carlo methods for Multitarget Filtering with Random Finite Sets
"... Abstract — Random finite sets are natural representations of multitarget states and observations that allow multisensor multitarget filtering to fit in the unifying random set framework for Data Fusion. Although the foundation has been established in the form of Finite Set Statistics (FISST), its ..."
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Abstract — Random finite sets are natural representations of multitarget states and observations that allow multisensor multitarget filtering to fit in the unifying random set framework for Data Fusion. Although the foundation has been established in the form of Finite Set Statistics (FISST), its relationship to conventional probability is not clear. Furthermore, optimal Bayesian multitarget filtering is not yet practical due to the inherent computational hurdle. Even the Probability Hypothesis Density (PHD) filter, which propagates only the first moment (or PHD) instead of the full multitarget posterior, still involves multiple integrals with no closed forms in general. This article establishes the relationship between FISST and conventional probability that leads to the development of a sequential Monte Carlo (SMC) multitarget filter. In addition, a SMC implementation of the PHD filter is proposed and demonstrated on a number of simulated scenarios. Both of the proposed filters are suitable for problems involving nonlinear nonGaussian dynamics. Convergence results for these filters are also established.
Engineering at Politecnico di Torino (Italy), where he received his Dr. Engr. degree in
, 705
"... MARCO LOPS was born and educated in Napoli (Italy). He got his “Laurea ” and his PhD degrees, both in Electronic and Computer Engineering, from the “Federico II” University. After a short experience at Selenia (currently Selex) as a Radar Engineer, he ..."
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MARCO LOPS was born and educated in Napoli (Italy). He got his “Laurea ” and his PhD degrees, both in Electronic and Computer Engineering, from the “Federico II” University. After a short experience at Selenia (currently Selex) as a Radar Engineer, he