Results 1  10
of
179
Fading Channels: InformationTheoretic And Communications Aspects
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 1998
"... In this paper we review the most peculiar and interesting informationtheoretic and communications features of fading channels. We first describe the statistical models of fading channels which are frequently used in the analysis and design of communication systems. Next, we focus on the information ..."
Abstract

Cited by 306 (1 self)
 Add to MetaCart
(Show Context)
In this paper we review the most peculiar and interesting informationtheoretic and communications features of fading channels. We first describe the statistical models of fading channels which are frequently used in the analysis and design of communication systems. Next, we focus on the information theory of fading channels, by emphasizing capacity as the most important performance measure. Both singleuser and multiuser transmission are examined. Further, we describe how the structure of fading channels impacts code design, and finally overview equalization of fading multipath channels.
Adaptive Sampling With the Ensemble Transform . . .
, 2001
"... A suboptimal Kalman filter called the ensemble transform Kalman filter (ET KF) is introduced. Like other Kalman filters, it provides a framework for assimilating observations and also for estimating the effect of observations on forecast error covariance. It differs from other ensemble Kalman filt ..."
Abstract

Cited by 210 (15 self)
 Add to MetaCart
A suboptimal Kalman filter called the ensemble transform Kalman filter (ET KF) is introduced. Like other Kalman filters, it provides a framework for assimilating observations and also for estimating the effect of observations on forecast error covariance. It differs from other ensemble Kalman filters in that it uses ensemble transformation and a normalization to rapidly obtain the prediction error covariance matrix associated with a particular deployment of observational resources. This rapidity enables it to quickly assess the ability of a large number of future feasible sequences of observational networks to reduce forecast error variance. The ET KF was used by the National Centers for Environmental Prediction in the Winter Storm Reconnaissance missions of 1999 and 2000 to determine where aircraft should deploy dropwindsondes in order to improve 2472h forecasts over the continental United States. The ET KF may be applied to any wellconstructed set of ensemble perturbations. The ET KF
Recovering 3D Shape and Motion from Image Streams using NonLinear Least Squares
, 1993
"... The simultaneous recovery of 3D shape and motion from image sequences is one of the more difficult problems in computer vision. Classical approaches to the problem rely on using algebraic techniques to solve for these unknowns given two or more images. More recently, a batch analysis of image stream ..."
Abstract

Cited by 198 (32 self)
 Add to MetaCart
(Show Context)
The simultaneous recovery of 3D shape and motion from image sequences is one of the more difficult problems in computer vision. Classical approaches to the problem rely on using algebraic techniques to solve for these unknowns given two or more images. More recently, a batch analysis of image streams (the temporal tracks of distinguishable image features) under orthography has resulted in highly accurate reconstructions. We generalize this approach to perspective projection and partial or uncertain tracks by using a nonlinear least squares technique. While our approach requires iteration, it quickly converges to the desired solution, even in the absence of a priori knowledge about the shape or motion. Important features of the algorithm include its ability to handle partial point tracks, to use line segment matches and point matches simultaneously, and to use an objectcentered representation for faster and more accurate structure and motion recovery. We also show how a projective (a...
iSAM: Incremental Smoothing and Mapping
, 2008
"... We present incremental smoothing and mapping (iSAM), a novel approach to the simultaneous localization and mapping problem that is based on fast incremental matrix factorization. iSAM provides an efficient and exact solution by updating a QR factorization of the naturally sparse smoothing informatio ..."
Abstract

Cited by 92 (27 self)
 Add to MetaCart
We present incremental smoothing and mapping (iSAM), a novel approach to the simultaneous localization and mapping problem that is based on fast incremental matrix factorization. iSAM provides an efficient and exact solution by updating a QR factorization of the naturally sparse smoothing information matrix, therefore recalculating only the matrix entries that actually change. iSAM is efficient even for robot trajectories with many loops as it avoids unnecessary fillin in the factor matrix by periodic variable reordering. Also, to enable data association in realtime, we provide efficient algorithms to access the estimation uncertainties of interest based on the factored information matrix. We systematically evaluate the different components of iSAM as well as the overall algorithm using various simulated and realworld datasets for both landmark and poseonly settings.
Square Root SAM: Simultaneous localization and mapping via square root information smoothing
 International Journal of Robotics Reasearch
, 2006
"... Solving the SLAM problem is one way to enable a robot to explore, map, and navigate in a previously unknown environment. We investigate smoothing approaches as a viable alternative to extended Kalman filterbased solutions to the problem. In particular, we look at approaches that factorize either th ..."
Abstract

Cited by 91 (32 self)
 Add to MetaCart
(Show Context)
Solving the SLAM problem is one way to enable a robot to explore, map, and navigate in a previously unknown environment. We investigate smoothing approaches as a viable alternative to extended Kalman filterbased solutions to the problem. In particular, we look at approaches that factorize either the associated information matrix or the measurement Jacobian into square root form. Such techniques have several significant advantages over the EKF: they are faster yet exact, they can be used in either batch or incremental mode, are better equipped to deal with nonlinear process and measurement models, and yield the entire robot trajectory, at lower cost for a large class of SLAM problems. In addition, in an indirect but dramatic way, column ordering heuristics automatically exploit the locality inherent in the geographic nature of the SLAM problem. In this paper we present the theory underlying these methods, along with an interpretation of factorization in terms of the graphical model associated with the SLAM problem. We present both simulation results and actual SLAM experiments in largescale environments that underscore the potential of these methods as an alternative to EKFbased approaches. 1
Ensemble Square Root Filters
, 2003
"... Ensemble data assimilation methods assimilate observations using statespace estimation methods and lowrank representations of forecast and analysis error covariances. A key element of such methods is the transformation of the forecast ensemble into an analysis ensemble with appropriate statistics ..."
Abstract

Cited by 78 (7 self)
 Add to MetaCart
Ensemble data assimilation methods assimilate observations using statespace estimation methods and lowrank representations of forecast and analysis error covariances. A key element of such methods is the transformation of the forecast ensemble into an analysis ensemble with appropriate statistics. This transformation may be performed stochastically by treating observations as random variables, or deterministically by requiring that the updated analysis perturbations satisfy the Kalman filter analysis error covariance equation. Deterministic analysis ensemble updates are implementations of Kalman square root filters. The nonuniqueness of the deterministic transformation used in square root Kalman filters provides a framework to compare three recently proposed ensemble data assimilation methods.
Using the Extended Kalman Filter with a Multilayer QuasiGeostrophic Ocean Model
 J. Geophys. Res
, 1992
"... this paper the extended Kalman filter is used with a nonlinear multilayer quasigeostrophic (QG) model. This provides us with both a realistic ocean model and a very sophisticated error statistics scheme. The extended Kalman filter is an extension of the common Kalman filter and may be used when the ..."
Abstract

Cited by 71 (16 self)
 Add to MetaCart
(Show Context)
this paper the extended Kalman filter is used with a nonlinear multilayer quasigeostrophic (QG) model. This provides us with both a realistic ocean model and a very sophisticated error statistics scheme. The extended Kalman filter is an extension of the common Kalman filter and may be used when the model dynamics or the measurement equation is nonlinear. It consists of an approximative equation for the propagation of error covariances, and also approximative filter equations if the measurement equation is nonlinear. When changing from a linear system to nonlinear dynamics the possible existence of a wide variety of phenomena which are nonexistent in the linear theory is introduced. Nonlinear systems may have solutions with multiple equilibria, where the solutions sometimes abruptly undergo transitions from one equilibrium to another as parameters change (bifurcations). Also chaotic behavior occurs in many deterministic systems, where solutions exhibit an apparently random behavior. The Lorenz [1963] model is probably the best known example of chaotic systems. It has solutions which undergo "unpredictable" transitions between two different equilibria (chaos). As discussed by Miller and Ghil
Realtime Obstacle Avoidance Using Central Flow Divergence and Peripheral Flow
 IEEE Transactions on Robotics and Automation
, 1995
"... The lure of using motion vision as a fundamental element in the perception of space drives this effort to use flow features as the sole cues for robot mobility. ..."
Abstract

Cited by 58 (5 self)
 Add to MetaCart
(Show Context)
The lure of using motion vision as a fundamental element in the perception of space drives this effort to use flow features as the sole cues for robot mobility.
Robust Shape Recovery from Occluding Contours Using a Linear Smoother
, 1993
"... Recovering the shape of an object from two views fails at occluding contours of smooth objects because the extremal contours are view dependent. For three or more views, shape recovery is possible, and several algorithms have recently been developed for this purpose. We present a new approach to the ..."
Abstract

Cited by 46 (9 self)
 Add to MetaCart
(Show Context)
Recovering the shape of an object from two views fails at occluding contours of smooth objects because the extremal contours are view dependent. For three or more views, shape recovery is possible, and several algorithms have recently been developed for this purpose. We present a new approach to the multiframe stereo problem which does not depend on differential measurements in the image, which may be noise sensitive. Instead, we use a linear smoother to optimally combine all of the measurements available at the contours (and other edges) in all of the images. This allows us to extract a robust and dense estimate of surface shape, and to integrate shape information from both surface markings and occluding contours. Keywords: Computer vision, image sequence analysis, motion analysis and multiframe stereo, shape and object representation, occluding contours (profiles). c flDigital Equipment Corporation 1993. All rights reserved. 1 Computer and Information Science Department, University...
Probabilistic Models of Appearance for 3D Object Recognition
 Int. J. Computer Vision
, 2000
"... We describe how to model the appearance of a 3D object using multiple views, learn such a model from training images, and use the model for object recognition. The model uses probability distributions to describe the range of possible variation in the object’s appearance. These distributions are ..."
Abstract

Cited by 39 (3 self)
 Add to MetaCart
We describe how to model the appearance of a 3D object using multiple views, learn such a model from training images, and use the model for object recognition. The model uses probability distributions to describe the range of possible variation in the object’s appearance. These distributions are organized on two levels. Large variations are handled by partitioning training images into clusters corresponding to distinctly different views of the object. Within each cluster, smaller variations are represented by distributions characterizing uncertainty in the presence, position, and measurements of various discrete features of appearance. Many types of features are used, ranging in abstraction from edge segments to perceptual groupings and regions. A matching procedure uses the feature uncertainty information to guide the search for a match between model and image. Hypothesized feature pairings are used to estimate a viewpoint transformation taking account of feature uncertainty. These methods have been implemented in an object recognition system, Oliver. Experiments show that Oliver is capable of learning to recognize complex objects in cluttered images, while acquiring models that represent those objects using relatively few views.