Results 1  10
of
16
A tutorial on particle filters for online nonlinear/nonGaussian Bayesian tracking
 IEEE TRANSACTIONS ON SIGNAL PROCESSING
, 2002
"... Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and nonGaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data online as it arrives, both from the point of view o ..."
Abstract

Cited by 1137 (2 self)
 Add to MetaCart
Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and nonGaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data online as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/nonGaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass (or “particle”) representations of probability densities, which can be applied to any statespace model and which generalize the traditional Kalman filtering methods. Several variants of the particle filter such as SIR, ASIR, and RPF are introduced within a generic framework of the sequential importance sampling (SIS) algorithm. These are discussed and compared with the standard EKF through an illustrative example.
Convergence of Sequential Monte Carlo Methods
 Sequential Monte Carlo Methods in Practice
, 2000
"... Bayesian estimation problems where the posterior distribution evolves over time through the accumulation of data arise in many applications in statistics and related fields. Recently, a large number of algorithms and applications based on sequential Monte Carlo methods (also known as particle filter ..."
Abstract

Cited by 140 (11 self)
 Add to MetaCart
Bayesian estimation problems where the posterior distribution evolves over time through the accumulation of data arise in many applications in statistics and related fields. Recently, a large number of algorithms and applications based on sequential Monte Carlo methods (also known as particle filtering methods) have appeared in the literature to solve this class of problems; see (Doucet, de Freitas & Gordon, 2001) for a survey. However, few of these methods have been proved to converge rigorously. The purpose of this paper is to address this issue. We present a general sequential Monte Carlo (SMC) method which includes most of the important features present in current SMC methods. This method generalizes and encompasses many recent algorithms. Under mild regularity conditions, we obtain rigorous convergence results for this general SMC method and therefore give theoretical backing for the validity of all the algorithms that can be obtained as particular cases of it. Keywords: Bayesian...
Monte Carlo smoothing for nonlinear time series
 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 2004
"... We develop methods for performing smoothing computations in general statespace models. The methods rely on a particle representation of the filtering distributions, and their evolution through time using sequential importance sampling and resampling ideas. In particular, novel techniques are pr ..."
Abstract

Cited by 95 (14 self)
 Add to MetaCart
We develop methods for performing smoothing computations in general statespace models. The methods rely on a particle representation of the filtering distributions, and their evolution through time using sequential importance sampling and resampling ideas. In particular, novel techniques are presented for generation of sample realizations of historical state sequences. This is carried out in a forwardfiltering backwardsmoothing procedure which can be viewed as the nonlinear, nonGaussian counterpart of standard Kalman filterbased simulation smoothers in the linear Gaussian case. Convergence in the meansquared error sense of the smoothed trajectories is proved, showing the validity of our proposed method. The methods are tested in a substantial application for the processing of speech signals represented by a timevarying autoregression and parameterised in terms of timevarying partial correlation coe#cients, comparing the results of our algorithm with those from a simple smoother based upon the filtered trajectories.
Discrete Filtering Using Branching and Interacting Particle Systems
, 1998
"... The stochastic filtering problem deals with the estimation of the current state of a signal process given the information supplied by an associate process, usually called the observation process. We describe a particle algorithm designed for solving numerically discrete filtering problems. The algor ..."
Abstract

Cited by 34 (3 self)
 Add to MetaCart
The stochastic filtering problem deals with the estimation of the current state of a signal process given the information supplied by an associate process, usually called the observation process. We describe a particle algorithm designed for solving numerically discrete filtering problems. The algorithm involves the use of a system of n particles which evolve (mutate) in correlation with each other (interact) according to law of the signal process and, at fixed times, give birth to a number of offsprings depending on the observation process. We present several possible branching mechanisms and prove, in a general context the convergence of the particle systems (as n tends to 1) to the conditional distribution of the signal given the observation. We then apply the result to the discrete filtering and give several example when the results can be applied. AMS Subject Classification (1991): 93E11, 60G57, 65U05 Keywords: Filtering, Particle Systems, Branching Algorithms, Interacting Algor...
Central Limit Theorem for Non Linear Filtering and Interacting Particle Systems
 Ann. Appl. Probab
, 1999
"... Several random particle systems approaches were recently suggested to solve numerically non linear filtering problems. The present analysis is concerned with genetictype interacting particle systems. Our aim is to study the fluctuations on path space of such particle approximating models. Keywords ..."
Abstract

Cited by 26 (6 self)
 Add to MetaCart
Several random particle systems approaches were recently suggested to solve numerically non linear filtering problems. The present analysis is concerned with genetictype interacting particle systems. Our aim is to study the fluctuations on path space of such particle approximating models. Keywords : Central Limit, Interacting random processes, Filtering, Stochastic approximation. code A.M.S : 60F05, 60G35, 93E11, 62L20. 1 Introduction 1.1 Background and motivations The Non Linear Filtering problem consists in recursively computing the conditional distributions of a non linear signal given its noisy observations. This problem has been extensively studied in the literature and, with the notable exception of the linearGaussian situation or wider classes of models (B`enes filters [2]) optimal filters have no finitely recursive solution (ChaleyatMaurel /Michel [7]). Although Kalman filtering ([26],[29]) is a popular tool in handling estimation problems its optimality heavily depends on...
Large Deviations for Interacting Particle Systems. Applications to Non Linear Filtering
 Stochastic Processes and their Applications
, 1997
"... The non linear filtering problem consists in computing the conditional distributions of a Markov signal process given its noisy observations. The dynamical structure of such distributions can be modelled by a measure valued dynamical Markov process. Several random particle approximations were recent ..."
Abstract

Cited by 20 (5 self)
 Add to MetaCart
The non linear filtering problem consists in computing the conditional distributions of a Markov signal process given its noisy observations. The dynamical structure of such distributions can be modelled by a measure valued dynamical Markov process. Several random particle approximations were recently suggested to approximate recursively in time the socalled non linear filtering equations. We present an interacting particle system approach and we develop large deviations principles for the empirical measures of the particle systems. We end this paper extending the results to an interacting particle system approach which includes branchings. Keywords : Large deviations, Interacting random processes, Filtering, Stochastic approximation. code A.M.S : 60F10, 60H10, 60G35, 93E11, 62L20. 1 Introduction 1.1 Background and motivations The non linear filtering problem consists in computing the conditional distribution of a signal given its noisy observation. Roughly speaking, a basic model...
Interacting Particle Systems Approximations of the Kushner Stratonovitch Equation
 Advances in Applied Probability
, 1999
"... In this paper we consider the continuous time filtering problem and we estimate the order of convergence of an interacting particle system scheme presented by the authors in previous works. We will discuss how the discrete time approximating model of the KushnerStratonovitch equation and the geneti ..."
Abstract

Cited by 10 (3 self)
 Add to MetaCart
In this paper we consider the continuous time filtering problem and we estimate the order of convergence of an interacting particle system scheme presented by the authors in previous works. We will discuss how the discrete time approximating model of the KushnerStratonovitch equation and the genetic type interacting particle system approximation combine. We present quenched error bounds as well as mean order converge results. Keywords: Non Linear Filtering, Filter approximation, error bounds, interacting particle systems, genetic algorithms. Code A.M.S: 60G35,93E11, 62L20. 1 Introduction 1.1 Background and Motivations The aim of this work is the design of an interacting particle system approach for the numerical solving of the continuous time non linear filtering problem. Recently intense interest has been aroused in the non linear filtering theory community concerning the connections between non linear estimation, measure valued processes and branching and interacting particle sy...
An Analysis of Regularized Interacting Particle Methods for Nonlinear Filtering
 Proceedings of the 3rd IEEE European Workshop on ComputerIntensive Methods in Control and Signal Processing
, 1998
"... Interacting particle methods have been recently proposed for the approximation of nonlinear filters. These are efficient recursive Monte Carlo methods, which in principle could be implemented in high dimensional problems  i.e. which could beat the curse of dimensionality  and where the particl ..."
Abstract

Cited by 9 (1 self)
 Add to MetaCart
Interacting particle methods have been recently proposed for the approximation of nonlinear filters. These are efficient recursive Monte Carlo methods, which in principle could be implemented in high dimensional problems  i.e. which could beat the curse of dimensionality  and where the particles automatically concentrate in regions of interest of the state space. In this paper we show that it is sometimes necessary to add a regularization step, and we analyze the approximation error for the resulting regularized interacting particle methods. 1. Introduction We consider the following model, where the unobserved state process fX t ; t 0g satisfies the stochastic differential equation (SDE) on R m dX t = b(X t ) dt + oe(X t ) dW t ; X 0 ¸ ¯ 0 ; (1) with standard Wiener process fW t ; t 0g, and where ddimensional observations fzn ; n 1g are available at discrete time instants 0 ! t 1 ! \Delta \Delta \Delta ! t n ! \Delta \Delta \Delta z n = h(X t n ) + v n ; in additional w...
Convergence of Empirical Processes for Interacting Particle Systems with Applications to Nonlinear Filtering
 Journal of Theoret. Probability
, 2000
"... In this paper, we investigate the convergence of empirical processes for a class of interacting particle numerical schemes arising in biology, genetic algorithms and advanced signal processing. The GlivenkoCantelli and Donsker theorems presented in this work extend the corresponding statements in t ..."
Abstract

Cited by 6 (3 self)
 Add to MetaCart
In this paper, we investigate the convergence of empirical processes for a class of interacting particle numerical schemes arising in biology, genetic algorithms and advanced signal processing. The GlivenkoCantelli and Donsker theorems presented in this work extend the corresponding statements in the classical theory and apply to a class of genetic type particle numerical schemes of the nonlinear filtering equation. Keywords : Empirical processes, Interacting particle systems, GlivenkoCantelli and Donsker theorems. code A.M.S : 60G35, 92D25 UMR C55830, CNRS, Univ. PaulSabatier, 31062 Toulouse, delmoral@cict.fr y ledoux@cict.fr 1 Introduction 1.1 Background and Motivations Let E be a Polish space endowed with its Borel oefield B(E). We denote by M 1 (E) the space of all probability measures on E equipped with the weak topology. We recall that the weak topology is generated by the bounded continuous functions on E and we denote by C b (E) the space of these functions. Let O...
Modeling Genetic Algorithms with Interacting Particle Systems
 In Theoretical Aspects of Evolutionary Computing
, 2001
"... We present in this work a natural Interacting Particle System (IPS) approach for modeling and studying the asymptotic behavior of Genetic Algorithms (GAs). In this model, a population is seen as a distribution (or measure) on the search space, and the Genetic Algorithm as a measure valued dynamical ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
We present in this work a natural Interacting Particle System (IPS) approach for modeling and studying the asymptotic behavior of Genetic Algorithms (GAs). In this model, a population is seen as a distribution (or measure) on the search space, and the Genetic Algorithm as a measure valued dynamical system. This model allows one to apply recent convergence results from the IPS literature for studying the convergence of genetic algorithms when the size of the population tends to infinity. We first review a number of approaches to Genetic Algorithms modeling and related convergence results. We then describe a general and abstract discrete time Interacting Particle System model for GAs, an we propose a brief review of some recent asymptotic results about the convergence of the NIPS approximating model (of finite Nsizedpopulation GAs) towards the IPS model (of infinite population GAs), including law of large number theorems, IL p mean and exponential bounds as well as large deviations...