Multi-target particle filtering for the probability hypothesis density

Sidenbladh, Hedvig

doi:10.1109/icif.2003.177321

Cited by 194 publications

(113 citation statements)

References 15 publications

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“…Beginning with Sidenbladh [16] and Zajic and Mahler [25], most researchers have implemented the PHD filter using SMC methods. SMC techniques were originally devised to approximate probability densities and the single-target Bayes filter.…”

Section: Methodology a Macroscopic Traffic Modelmentioning

confidence: 99%

A probabilistic hypothesis density filter for traffic flow estimation in the presence of clutter

Canaud

Mihaylova

Faouzi

et al. 2012

2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)

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Abstract-Prediction of traffic flow variables such as traffic volume, travel speed or travel time for a short time horizon is of paramount importance in traffic control. Hence, the data assimilation process in traffic modeling for estimation and prediction plays a key role. However, the increasing complexity, non-linearity and presence of various uncertainties (both in the measured data and models) are important factors affecting the traffic state prediction. To overcome this problem, new methodologies have been proposed. With this aim, in this paper we propose the use of the Probability Hypothesis Density (PHD) filter for traffic estimation. This methology is intensively studied, developed and improved for the purposes of multiple object tracking and consists in the recursive state estimation of several targets by using the information coming from an observation process. However, some issues need to be studied, especially the impact of the clutter (false alarm) intensity. The goal of this paper is to expose the potential of the PHD filters for real-time traffic state estimation and the choice of an appropriate clutter intensity. This investigation is based on a Cell Transmission Model (CTM) coupled with the PHD filter. It brings a novel tool to the state estimation problem and allows one to estimate the densities in traffic networks. In this work, we compare this PHD filter with the particle filter (PF) which has been successfully applied in traffic control and conclude that the PHD filter can be seen as a relevant alternative that opens new research avenues.

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Section: Methodology a Macroscopic Traffic Modelmentioning

confidence: 99%

A probabilistic hypothesis density filter for traffic flow estimation in the presence of clutter

Canaud

Mihaylova

Faouzi

et al. 2012

2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)

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“…A tractable implementation of the framework is to use the first order moment of the multi-target posterior, the probability hypothesis density (PHD) [15] as an approximation. SMC based implementations of the PHD have been reported, for example, in the articles [16] [17].…”

Section: Approaches To Tracking Unknown Number Of Targetsmentioning

confidence: 99%

Rao-Blackwellized particle filter for multiple target tracking

Särkkä¹,

Vehtari²,

Lampinen³

2007

Information Fusion

252

213

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“…Moreover, the number of particles can be adapted to maintain a constant ratio of particles to expected number of targets. This approach first appeared in [46] around the same time as two other independent works [40] and [48]. In [40], only the special case without clutter for ground target filtering was considered.…”

Section: Introductionmentioning

confidence: 99%

“…This approach first appeared in [46] around the same time as two other independent works [40] and [48]. In [40], only the special case without clutter for ground target filtering was considered. On the other hand, [48] describes an implementation for the special case with neither birth nor spawning.…”

Section: Introductionmentioning

confidence: 99%

Sequential monte carlo methods for multi-target filtering with random finite sets

Singh

Doucet

2005

IEEE Trans. Aerosp. Electron. Syst.

988

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Abstract-Random finite sets are natural representations of multi-target states and observations that allow multi-sensor multi-target 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 multi-target 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 multi-target 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) multi-target 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 non-linear non-Gaussian dynamics. Convergence results for these filters are also established.

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Multi-target particle filtering for the probability hypothesis density

Cited by 194 publications

References 15 publications

A probabilistic hypothesis density filter for traffic flow estimation in the presence of clutter

A probabilistic hypothesis density filter for traffic flow estimation in the presence of clutter

Rao-Blackwellized particle filter for multiple target tracking

Sequential monte carlo methods for multi-target filtering with random finite sets

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