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247
A tutorial on particle filtering and smoothing: fifteen years later
 OXFORD HANDBOOK OF NONLINEAR FILTERING
, 2011
"... Optimal estimation problems for nonlinear nonGaussian statespace models do not typically admit analytic solutions. Since their introduction in 1993, particle filtering methods have become a very popular class of algorithms to solve these estimation problems numerically in an online manner, i.e. r ..."
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Cited by 214 (15 self)
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Optimal estimation problems for nonlinear nonGaussian statespace models do not typically admit analytic solutions. Since their introduction in 1993, particle filtering methods have become a very popular class of algorithms to solve these estimation problems numerically in an online manner, i.e. recursively as observations become available, and are now routinely used in fields as diverse as computer vision, econometrics, robotics and navigation. The objective of this tutorial is to provide a complete, uptodate survey of this field as of 2008. Basic and advanced particle methods for filtering as well as smoothing are presented.
Tracking a moving object with a binary sensor network
, 2003
"... In this paper we examine the role of very simple and noisy sensors for the tracking problem. We propose a binary sensor model, where each sensor’s value is converted reliably to one bit of information only: whether the object is moving toward the sensor or away from the sensor. We show that a networ ..."
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Cited by 159 (1 self)
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In this paper we examine the role of very simple and noisy sensors for the tracking problem. We propose a binary sensor model, where each sensor’s value is converted reliably to one bit of information only: whether the object is moving toward the sensor or away from the sensor. We show that a network of binary sensors has geometric properties that can be used to develop a solution for tracking with binary sensors and present resulting algorithms and simulation experiments. We develop a particle filtering style algorithm for target tracking using such minimalist sensors. We present an analysis of a fundamental tracking limitation under this sensor model, and show how this limitation can be overcome through the use of a single bit of proximity information at each sensor node. Our extensive simulations show low error that decreases with sensor density. 1.
Simultaneous Localisation and Mapping (SLAM): Part I The Essential Algorithms
 IEEE ROBOTICS AND AUTOMATION MAGAZINE
, 2006
"... This tutorial provides an introduction to Simultaneous Localisation and Mapping (SLAM) and the extensive research on SLAM that has been undertaken over the past decade. SLAM is the process by which a mobile robot can build a map of an environment and at the same time use this map to compute it’s own ..."
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Cited by 102 (2 self)
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This tutorial provides an introduction to Simultaneous Localisation and Mapping (SLAM) and the extensive research on SLAM that has been undertaken over the past decade. SLAM is the process by which a mobile robot can build a map of an environment and at the same time use this map to compute it’s own location. The past decade has seen rapid and exciting progress in solving the SLAM problem together with many compelling implementations of SLAM methods. Part I of this tutorial (this paper), describes the probabilistic form of the SLAM problem, essential solution methods and significant implementations. Part II of this tutorial will be concerned with recent advances in computational methods and new formulations of the SLAM problem for large scale and complex environments.
Information fusion for wireless sensor networks: methods, models, and classifications,”
 Article ID 1267073,
, 2007
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Multimodal fusion for multimedia analysis: a survey
, 2010
"... This survey aims at providing multimedia researchers with a stateoftheart overview of fusion strategies, which are used for combining multiple modalities in order to accomplish various multimedia analysis tasks. The existing literature on multimodal fusion research is presented through several c ..."
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Cited by 58 (1 self)
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This survey aims at providing multimedia researchers with a stateoftheart overview of fusion strategies, which are used for combining multiple modalities in order to accomplish various multimedia analysis tasks. The existing literature on multimodal fusion research is presented through several classifications based on the fusion methodology and the level of fusion (feature, decision, and hybrid). The fusion methods are described from the perspective of the basic concept, advantages, weaknesses, and their usage in various analysis tasks as reported in the literature. Moreover, several distinctive issues that influence a multimodal fusion process such as, the use of correlation and independence, confidence level, contextual information, synchronization between different modalities, and the optimal modality selection are also highlighted. Finally, we present the open issues for further research in the area of multimodal fusion.
Mobile Sensor Network Control using Mutual Information Methods and Particle Filters
"... This paper develops a set of methods enabling an informationtheoretic distributed control architecture to facilitate search by a mobile sensor network. Given a particular configuration of sensors, this technique exploits the structure of the probability distributions of the target state and of the ..."
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Cited by 56 (3 self)
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This paper develops a set of methods enabling an informationtheoretic distributed control architecture to facilitate search by a mobile sensor network. Given a particular configuration of sensors, this technique exploits the structure of the probability distributions of the target state and of the sensor measurements to control the mobile sensors such that future observations minimize the expected future uncertainty of the target state. The mutual information between the sensors and the target state is computed using a particle filter representation of the posterior probability distribution, making it possible to directly use nonlinear and nonGaussian target state and sensor models. To make the approach scalable to increasing network sizes, singlenode and pairwisenode approximations to the mutual information are derived, with analytically bounded error. The pairwisenode approximation is proven to be a more accurate objective function than the singlenode approximation. The mobile sensors are cooperatively controlled using a distributed optimization, yielding coordinated motion of the network. The consequences of using these methods are explored for various sensing modalities, including bearingsonly sensing, rangeonly sensing, and magnetic field sensing, all with potential for search and rescue applications. For each sensing modality, the behavior of this nonparametric method is compared and contrasted with the results of linearized methods, and simulations are performed of a target search using the dynamics of actual vehicles. Monte Carlo results demonstrate that as network size increases, the sensors
Limit theorems for weighted samples with applications to Sequential Monte Carlo Methods
, 2005
"... In the last decade, sequential MonteCarlo methods (SMC) emerged as a key tool in computational statistics (see for instance Doucet et al. (2001), Liu (2001), Künsch (2001)). These algorithms approximate a sequence of distributions by a sequence of weighted empirical measures associated to a weighte ..."
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Cited by 55 (14 self)
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In the last decade, sequential MonteCarlo methods (SMC) emerged as a key tool in computational statistics (see for instance Doucet et al. (2001), Liu (2001), Künsch (2001)). These algorithms approximate a sequence of distributions by a sequence of weighted empirical measures associated to a weighted population of particles. These particles and weights are generated recursively according to elementary transformations: mutation and selection. Examples of applications include the sequential MonteCarlo techniques to solve optimal nonlinear filtering problems in statespace models, molecular simulation, genetic optimization, etc. Despite many theoretical advances (see for instance Gilks and Berzuini (2001), Künsch (2003), Del Moral (2004), Chopin (2004)), the asymptotic property of these approximations remains of course a question of central interest. In this paper, we analyze sequential Monte Carlo methods from an asymptotic perspective, that is, we establish law of large numbers and invariance principle as the number of particles gets large. We introduce the concepts of weighted sample consistency and asymptotic normality, and derive conditions under which the mutation and the selection procedure used in the sequential MonteCarlo buildup preserve these properties. To illustrate our findings, we analyze SMC algorithms to approximate the filtering distribution in statespace models. We show how our techniques allow to relax restrictive technical conditions used in previously reported works and provide grounds to analyze more sophisticated sequential sampling strategies.
An Overview of Sequential Monte Carlo Methods for Parameter Estimation
 in General StateSpace Models,” in IFAC System Identification, no. Ml
, 2009
"... Abstract: Nonlinear nonGaussian statespace models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, provide very good numerical approximations to the associated optimal state estimation problems. However, in many ..."
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Cited by 48 (6 self)
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Abstract: Nonlinear nonGaussian statespace models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, provide very good numerical approximations to the associated optimal state estimation problems. However, in many scenarios, the statespace model of interest also depends on unknown static parameters that need to be estimated from the data. In this context, standard SMC methods fail and it is necessary to rely on more sophisticated algorithms. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed to perform static parameter estimation in general statespace models. We discuss the advantages and limitations of these methods.
Particle filter theory and practice with positioning applications
 IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE
, 2010
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