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26
On Sequential Monte Carlo Sampling Methods for Bayesian Filtering
 STATISTICS AND COMPUTING
, 2000
"... In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and nonGaussian. A general importance sampling framework is develop ..."
Abstract

Cited by 660 (63 self)
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In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and nonGaussian. A general importance sampling framework is developed that unifies many of the methods which have been proposed over the last few decades in several different scientific disciplines. Novel extensions to the existing methods are also proposed. We show in particular how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature; these lead to very effective importance distributions. Furthermore we describe a method which uses RaoBlackwellisation in order to take advantage of the analytic structure present in some important classes of statespace models. In a final section we develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.
Architectures for Efficient Implementation of Particle Filters
, 2004
"... Particle filters are sequential Monte Carlo methods that are used in numerous problems where timevarying signals must be presented in real time and where the objective is to estimate various unknowns of the signal and/or detect events described by the signals. The standard solutions of such proble ..."
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Cited by 18 (0 self)
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Particle filters are sequential Monte Carlo methods that are used in numerous problems where timevarying signals must be presented in real time and where the objective is to estimate various unknowns of the signal and/or detect events described by the signals. The standard solutions of such problems in many applications are based on the Kalman filters or extended Kalman filters. In situations when the problems are nonlinear or the noise that distorts the signals is nonGaussian, the Kalman filters provide a solution that may be far from optimal. Particle filters are an intriguing alternative to the Kalman filters due to their excellent performance in very di#cult problems including communications, signal processing, navigation, and computer vision. Hence, particle filters have been the focus of wide research recently and immense literature can be found on their theory. Most of these works recognize the complexity and computational intensity of these filters, but there has been no e#ort directed toward the implementation of these filters in hardware. The objective of this dissertation is to develop, design, and build e#cient hardware for particle filters, and thereby bring them closer to practical applications. The fact that particle filters outperform most of the traditional filtering methods in many complex practical scenarios, coupled with the challenges related to decreasing their computational complexity and improving realtime performance, makes this work worthwhile. The main
Kalman Filtering Using Pairwise Gaussian Models
 IN PROCEEDINGS OF THE ICASSP, HONGKONG, APRIL 610 2003
, 2003
"... An important problem in signal processing consists in recursively estimating an unobservable process x = {xn }n#IN from an observed process y = {yn }n#IN . This is done classically in the framework of Hidden Markov Models (HMM). In the linear Gaussian case, the classical recursive solution is giv ..."
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Cited by 16 (12 self)
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An important problem in signal processing consists in recursively estimating an unobservable process x = {xn }n#IN from an observed process y = {yn }n#IN . This is done classically in the framework of Hidden Markov Models (HMM). In the linear Gaussian case, the classical recursive solution is given by the wellknown Kalman filter. In this paper, we consider Pairwise Gaussian Models by assuming that the pair (x, y) is Markovian and Gaussian. We show that this model is strictly more general than the HMM, and yet still enables Kalmanlike filtering.
Prediction Of Final Data With Use Of Preliminary And/or Revised Data
 Journal of Forecasting
, 1995
"... : In the case of U.S. national accounts, the data are revised for the first few years and every decade, which implies that we do not really have the final data. In this paper, we aim to predict the final data, using the preliminary data and/or the revised data. The following predictors are introduce ..."
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Cited by 12 (4 self)
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: In the case of U.S. national accounts, the data are revised for the first few years and every decade, which implies that we do not really have the final data. In this paper, we aim to predict the final data, using the preliminary data and/or the revised data. The following predictors are introduced and derived from a context of the nonlinear filtering or smoothing problem, which are: (i) prediction of the final data of time t given the preliminary data up to time t
Constraintbased task specification and estimation for sensorbased robot systems in the presence of geometric uncertainty
 Int. J. Robotics Research
, 2007
"... This paper introduces a systematic constraintbased approach to specify complex tasks of general sensorbased robot systems consisting of rigid links and joints. The approach integrates both instantaneous task specification and estimation of geometric uncertainty in a unified framework. Major compone ..."
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Cited by 12 (8 self)
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This paper introduces a systematic constraintbased approach to specify complex tasks of general sensorbased robot systems consisting of rigid links and joints. The approach integrates both instantaneous task specification and estimation of geometric uncertainty in a unified framework. Major components are the use of feature coordinates, defined with respect to object and feature frames, which facilitate the task specification, and the introduction of uncertainty coordinates to model geometric uncertainty. While the focus of the paper is on task specification, an existing velocity based control scheme is reformulated in terms of these feature and uncertainty coordinates. This control scheme compensates for the effect of time varying uncertainty coordinates. Constraint weighting results in an invariant robot behavior in case of conflicting constraints with heterogeneous units. The approach applies to a large variety of robot systems (mobile robots, multiple robot systems, dynamic humanrobot interaction, etc.), various sensor systems, and different robot tasks. Ample simulation and experimental results are presented.
Methods and techniques of complex systems science: An overview
, 2003
"... In this chapter, I review the main methods and techniques of complex systems science. As a ..."
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Cited by 11 (0 self)
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In this chapter, I review the main methods and techniques of complex systems science. As a
Bayesian Estimation of DSGE Models: Lessons from SecondOrder Approximations.” Mimeo
, 2006
"... This paper investigates a general procedure to estimate secondorder approximations to a DSGE model and compares the performance with the widely used estimation technique for a loglinearized economy on a version of new Keynesian monetary model. It is done in the context of posterior distributions, ..."
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Cited by 9 (0 self)
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This paper investigates a general procedure to estimate secondorder approximations to a DSGE model and compares the performance with the widely used estimation technique for a loglinearized economy on a version of new Keynesian monetary model. It is done in the context of posterior distributions, welfare cost, and impulse response analysis. Our findings include the followings. First, we find that all the results of An and Schorfheide (2007) are confirmed with U.S. data. With the nonlinear estimation we can identify parameters that are neglected previously; the marginal data density evaluation shows that data support the nonlinear estimation procedure; and parameter estimates that are related to nondeterministic steady states are quite different from the linear estimates. Second, the estimated welfare differentials are more aggressive for the secondorder approximations, that is, the posterior welfare differentials from the linear estimation may underestimate the welfare cost resulted from changes in the monetary policy. Third, the secondorder approximation unveils quite different dynamics which are neglected in a loglinearized economy.
On Nonlinear and Nonnormal Filter Using Rejection Sampling
, 1999
"... In this paper, a nonlinear and/or nonnormal filter is proposed using rejection sampling. Generating random draws of the statevector directly from the filtering density, the filtering estimate is simply obtained as the arithmetic average of the random draws. In the proposed filter, the random draws ..."
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Cited by 6 (5 self)
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In this paper, a nonlinear and/or nonnormal filter is proposed using rejection sampling. Generating random draws of the statevector directly from the filtering density, the filtering estimate is simply obtained as the arithmetic average of the random draws. In the proposed filter, the random draws are recursively generated at each time. The MonteCarlo experiments indicate that the proposed nonlinear and nonnormal filter shows a good performance. Keywords Nonlinear, Nonnormal, Filtering, Rejection Sampling, Proposal Density. I. Introduction Nonlinear filters have been investigated for a long time (e.g., Alspach and Sorenson [1], Sorenson and Alspach [18] and Wishner, Tabaczynski and Athans [23]) and we still have numerous densitybased nonlinear filtering algorithms. Kitagawa [13] and Kramer and Sorenson [16] proposed the numerical integration procedure. Tanizaki [20] and Tanizaki and Mariano [22] utilized the MonteCarlo integration with importance sampling for nonlinear and no...
Nonlinear structural dynamical system identification using adaptive particle filters
 JOURNAL OF SOUND AND VIBRATION
, 2007
"... ..."
Distributed Particle Filters for Object Tracking in Sensor Networks
, 2005
"... A particle filter (PF) is a simulationbased algorithm used to solve estimation problems, such as object tracking. The PF works by maintaining a set of “particles ” as candidate state descriptions of an object’s position. The filter determines how well the set of particles describe the observations ..."
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Cited by 3 (0 self)
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A particle filter (PF) is a simulationbased algorithm used to solve estimation problems, such as object tracking. The PF works by maintaining a set of “particles ” as candidate state descriptions of an object’s position. The filter determines how well the set of particles describe the observations and fit the dynamic model, in order to form an object state estimate. The drawback of the basic PF is that the algorithm functions by collecting all data at a fusion centre. This leads to high communication and energy costs in a resourcelimited network such as the sensor network. In this thesis, we analyze the PF to determine how it can be modified for efficient use in a sensor network. Our main priority is to keep communication and energy costs low since this increases the network lifetime. We propose two innovative particle filtering algorithms which minimizes the associated costs. ii Sommaire Un filtre de particules (FP) est un algorithme utilisé pour résoudre des problèmes