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RaoBlackwellised Particle Filtering for Dynamic Bayesian Networks
"... Particle filters (PFs) are powerful samplingbased inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of probability distribution, nonlinearity and nonstationarity. They have appeared in several fields under such names as “conde ..."
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Cited by 348 (11 self)
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as “condensation”, “sequential Monte Carlo” and “survival of the fittest”. In this paper, we show how we can exploit the structure of the DBN to increase the efficiency of particle filtering, using a technique known as RaoBlackwellisation. Essentially, this samples some of the variables, and marginalizes out
RaoBlackwellised Particle Filtering for Fault Diagnosis
 IEEE Aerospace
, 2001
"... We tackle the fault diagnosis problem using conditionally Gaussian state space models and an efficient Monte Carlo method known as RaoBlackwellised particle filtering. In this setting, there is one different linearGaussian state space model for each possible discrete state of operation. The task of ..."
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Cited by 62 (1 self)
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We tackle the fault diagnosis problem using conditionally Gaussian state space models and an efficient Monte Carlo method known as RaoBlackwellised particle filtering. In this setting, there is one different linearGaussian state space model for each possible discrete state of operation. The task
RaoBlackwellised Particle Filtering via Data Augmentation
 In Advances in Neural Information Processing Systems
, 2001
"... In this paper, we extend the RaoBlackwellised particle filtering method to more complex hybrid models consisting of Gaussian latent variables and discrete observations. This is accomplished by augmenting the models with artificial variables that enable us to apply RaoBlackwellisation. Other improv ..."
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Cited by 11 (1 self)
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In this paper, we extend the RaoBlackwellised particle filtering method to more complex hybrid models consisting of Gaussian latent variables and discrete observations. This is accomplished by augmenting the models with artificial variables that enable us to apply RaoBlackwellisation. Other
RaoBlackwellised particle filtering for blind system identification
 In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
, 2005
"... This paper develops a RaoBlackwellised particle filtering algorithm for blind system identification. The state space model under consideration uses a timevarying autoregressive (AR) model for the sources, and a timevarying finite impulse response (FIR) model for the channel. The multisensor me ..."
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Cited by 2 (0 self)
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This paper develops a RaoBlackwellised particle filtering algorithm for blind system identification. The state space model under consideration uses a timevarying autoregressive (AR) model for the sources, and a timevarying finite impulse response (FIR) model for the channel. The multi
A modified RaoBlackwellised particle filter
 in Proceedings of the IEEE conference on Acoustics, Speech, and Signal Processing
, 2006
"... In this work, we present some examples of applications of the socalled RaoBlackwellised Particle Filter (RBPF). RBPFs are an extension to Particle Filters (PFs) which are applicable to conditionally linearGaussian statespace models. Although RBPF introductions and reviews may be found in many ex ..."
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Cited by 2 (0 self)
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In this work, we present some examples of applications of the socalled RaoBlackwellised Particle Filter (RBPF). RBPFs are an extension to Particle Filters (PFs) which are applicable to conditionally linearGaussian statespace models. Although RBPF introductions and reviews may be found in many
Exact Approximation of RaoBlackwellised Particle Filters?
"... for performing inference in nonlinear nonGaussian statespace models. The class of “RaoBlackwellised ” particle filters exploits the analytic marginalisation that is possible for some statespace models to reduce the variance of the Monte Carlo estimates. Despite being applicable to only a restri ..."
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Cited by 2 (1 self)
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for performing inference in nonlinear nonGaussian statespace models. The class of “RaoBlackwellised ” particle filters exploits the analytic marginalisation that is possible for some statespace models to reduce the variance of the Monte Carlo estimates. Despite being applicable to only a
RaoBlackwellised Particle Filter for Tracking with Application
 in Visual Surveillance”, the Second Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VSPETS), in conjunction with ICCV 2005, Oct
"... Particle filters have become popular tools for visual tracking since they do not require the modeling system to be Gaussian and linear. However, when applied to a high dimensional statespace, particle filters can be inefficient because a prohibitively large number of samples may be required in orde ..."
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Cited by 1 (1 self)
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in order to approximate the underlying density functions with desired accuracy. In this paper, by proposing a tracking algorithm based on RaoBlackwellised particle filter (RBPF), we show how to exploit the analytical relationship between state variables to improve the efficiency and accuracy of a regular
People Tracking with Anonymous and IDSensors Using RaoBlackwellised Particle Filters
, 2003
"... Estimating the location of people using a network of sensors placed throughout an environment is a fundamental challenge in ubiquitous computing and smart environments. Idsensors such as infrared badges provide explicit object identity information but coarse location information while anonymous sen ..."
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Cited by 78 (7 self)
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of idsensors. RaoBlackwellised particle filters are used to estimate object locations. Each particle represents the association history between Kalman filtered object tracks and observations. After using only anonymous sensors until id estimates are certain enough, id assignments are sampled as well
People Tracking with Anonymous and IDSensors Using RaoBlackwellised Particle Filters
"... Estimating the location of people using a network of sensors placed throughout an environment is a fundamental challenge in smart environments and ubiquitous computing. Idsensors such as infrared badges provide explicit object identity information but coarse location information while anonymous sen ..."
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of idsensors. RaoBlackwellised particle filters are used to estimate object locations. Each particle represents the association history between Kalman filtered object tracks and observations. After using only anonymous sensors until id estimates are certain enough, id assignments are sampled as well
Results 1  10
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