Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking

Mihaylova, Lyudmila; Carmi, Avishy; Septier, François; Gning, Amadou; Pang, Sze Kim; Godsill, Simon

doi:10.1016/j.dsp.2013.11.006

Cited by 164 publications

(105 citation statements)

References 78 publications

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“…A promising alternative class of methods for Bayesian filtering, known as Sequential Markov Chain Monte Carlo (SMCMC), has been proposed in several papers [13]- [17] and successfully applied to challenging applications [18], [19].…”

mentioning

confidence: 99%

Langevin and Hamiltonian Based Sequential MCMC for Efficient Bayesian Filtering in High-Dimensional Spaces

Septier

Peters²

2016

IEEE J. Sel. Top. Signal Process.

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Nonlinear non-Gaussian state-space models arise in numerous applications in statistics and signal processing. In this context, one of the most successful and popular approximation techniques is the Sequential Monte Carlo (SMC) algorithm, also known as particle filtering. Nevertheless, this method tends to be inefficient when applied to high dimensional problems.In this paper, we focus on another class of sequential inference methods, namely the Sequential Markov Chain Monte Carlo (SMCMC) techniques, which represent a promising alternative to SMC methods. After providing a unifying framework for the class of SMCMC approaches, we propose novel efficient strategies based on the principle of Langevin diffusion andHamiltonian dynamics in order to cope with the increasing number of high-dimensional applications. Simulation results show that the proposed algorithms achieve significantly better performance compared to existing algorithms.

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mentioning

confidence: 99%

Langevin and Hamiltonian Based Sequential MCMC for Efficient Bayesian Filtering in High-Dimensional Spaces

Septier

Peters²

2016

IEEE J. Sel. Top. Signal Process.

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“…The problem of tracking large groups is modelled in a similar way to extended object tracking (EOT) problems [2]. In [3] a Poisson likelihood model is used.…”

Section: Introductionmentioning

confidence: 99%

“…when multiple or a single measurement is observed, the object is referred to as an extended object or a point object, respectively [1]. A large group of point objects moving in a coordinated fashion may also be modelled as an extended object [2].…”

Section: Introductionmentioning

confidence: 99%

A Gaussian process approach for extended object tracking with random shapes and for dealing with intractable likelihoods

Aftab

Freitas

Arvaneh

et al. 2017

2017 22nd International Conference on Digital Signal Processing (DSP)

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Abstract-Tracking of arbitrarily shaped extended objects is a complex task due to the intractable analytical expression of measurement to object associations. The presence of sensor noise and clutter worsens the situation. Although a significant work has been done on the extended object tracking (EOT) problems, most of the developed methods are restricted by assumptions on the shape of the object such as stick, circle, or other axis-symmetric properties etc. This paper proposes a novel Gaussian process approach for tracking an extended object using a convolution particle filter (CPF). The new approach is shown to track irregularly shaped objects efficiently in presence of measurement noise and clutter. The mean recall and precision values for the shape, calculated by the proposed method on simulated data are around 0.9, respectively, by using 1000 particles.

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“…Groups are structured objects and formations of entities moving in a coordinated manner [1]. Limited by poor sensor resolution and little requirement in application, the objects to be tracked are considered as point sources in conventional sense [2].…”

Section: Introductionmentioning

confidence: 99%

Tracking Maneuvering Group Target with Extension Predicted and Best Model Augmentation Method Adapted

Linhai

Wang

2017

Mathematical Problems in Engineering

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The random matrix (RM) method is widely applied for group target tracking. The assumption that the group extension keeps invariant in conventional RM method is not yet valid, as the orientation of the group varies rapidly while it is maneuvering; thus, a new approach with group extension predicted is derived here. To match the group maneuvering, a best model augmentation (BMA) method is introduced. The existing BMA method uses a fixed basic model set, which may lead to a poor performance when it could not ensure basic coverage of true motion modes. Here, a maneuvering group target tracking algorithm is proposed, where the group extension prediction and the BMA adaption are exploited. The performance of the proposed algorithm will be illustrated by simulation.

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Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking

Cited by 164 publications

References 78 publications

Langevin and Hamiltonian Based Sequential MCMC for Efficient Bayesian Filtering in High-Dimensional Spaces

Langevin and Hamiltonian Based Sequential MCMC for Efficient Bayesian Filtering in High-Dimensional Spaces

A Gaussian process approach for extended object tracking with random shapes and for dealing with intractable likelihoods

Tracking Maneuvering Group Target with Extension Predicted and Best Model Augmentation Method Adapted

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