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607
Distributing the Kalman filters for largescale systems
 IEEE Trans. on Signal Processing, http://arxiv.org/pdf/0708.0242
"... Abstract—This paper presents a distributed Kalman filter to estimate the state of a sparsely connected, largescale,dimensional, dynamical system monitored by a network of sensors. Local Kalman filters are implemented ondimensional subsystems,, obtained by spatially decomposing the largescale sys ..."
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Cited by 55 (11 self)
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Abstract—This paper presents a distributed Kalman filter to estimate the state of a sparsely connected, largescale,dimensional, dynamical system monitored by a network of sensors. Local Kalman filters are implemented ondimensional subsystems,, obtained by spatially decomposing the largescale system. The distributed Kalman filter is optimal under an th order Gauss–Markov approximation to the centralized filter. We quantify the information loss due to this thorder approximation by the divergence, which decreases as increases. The order of the approximation leads to a bound on the dimension of the subsystems, hence, providing a criterion for subsystem selection. The (approximated) centralized Riccati and Lyapunov equations are computed iteratively with only local communication and loworder computation by a distributed iterate collapse inversion (DICI) algorithm. We fuse the observations that are common among the local Kalman filters using bipartite fusion graphs and consensus averaging algorithms. The proposed algorithm achieves full distribution of the Kalman filter. Nowhere in the network, storage, communication, or computation ofdimensional vectors and matrices is required; only dimensional vectors and matrices are communicated or used in the local computations at the sensors. In other words, knowledge of the state is itself distributed. Index Terms—Distributed algorithms, distributed estimation, information filters, iterative methods, Kalman filtering, largescale systems, matrix inversion, sparse matrices. I.
Planningbased Prediction for Pedestrians
"... Abstract — We present a novel approach for determining robot movements that efficiently accomplish the robot’s tasks while not hindering the movements of people within the environment. Our approach models the goaldirected trajectories of pedestrians using maximum entropy inverse optimal control. Th ..."
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Cited by 54 (15 self)
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Abstract — We present a novel approach for determining robot movements that efficiently accomplish the robot’s tasks while not hindering the movements of people within the environment. Our approach models the goaldirected trajectories of pedestrians using maximum entropy inverse optimal control. The advantage of this modeling approach is the generality of its learned cost function to changes in the environment and to entirely different environments. We employ the predictions of this model of pedestrian trajectories in a novel incremental planner and quantitatively show the improvement in hindrancesensitive robot trajectory planning provided by our approach. I.
Exploiting local low dimensionality of the atmospheric dynamics . . .
 PHYS. REV. LETT
, 2002
"... Recent studies (Patil et al. 2001, 2002) have shown that, when the Earth’s surface is divided up into local regions of moderate size, vectors of the forecast uncertainties in such regions tend to lie in a subspace of much lower dimension than that of the full atmospheric state vector. In this paper ..."
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Cited by 51 (17 self)
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Recent studies (Patil et al. 2001, 2002) have shown that, when the Earth’s surface is divided up into local regions of moderate size, vectors of the forecast uncertainties in such regions tend to lie in a subspace of much lower dimension than that of the full atmospheric state vector. In this paper we show how this finding can be exploited to formulate a potentially accurate and efficient data assimilation technique. The basic idea is that, since the expected forecast errors lie in a locally low dimensional subspace, the analysis resulting from the data assimilation should also lie in this subspace. This implies that operations only on relatively low dimensional matrices are required. The data assimilation analysis is done locally in a manner allowing massively parallel computation to be exploited. The local analyses are then used to construct global states for advancement to the next forecast time. Potential advantages of the method are discussed. 1
An optimal estimation approach to visual perception and learning
 VISION RESEARCH
, 1999
"... How does the visual system learn an internal model of the external environment? How is this internal model used during visual perception? How are occlusions and background clutter so effortlessly discounted for when recognizing a familiar object? How is a particular object of interest attended to an ..."
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Cited by 50 (7 self)
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How does the visual system learn an internal model of the external environment? How is this internal model used during visual perception? How are occlusions and background clutter so effortlessly discounted for when recognizing a familiar object? How is a particular object of interest attended to and recognized in the presence of other objects in the field of view? In this paper, we attempt to address these questions from the perspective of Bayesian optimal estimation theory. Using the concept of generative models and the statistical theory of Kalman filtering, we show how static and dynamic events occurring in the visual environment may be learned and recognized given only the input images. We also describe an extension of the Kalman filter model that can handle multiple objects in the field of view. The resulting robust Kalman filter model demonstrates how certain forms of attention can be viewed as an emergent property of the interaction between top–down expectations and bottom–up signals. Experimental results are provided to help demonstrate the ability of such a model to perform robust segmentation and recognition of objects and image sequences in the presence of occlusions and clutter.
Principles and Techniques for Sensor Data Fusion
, 1993
"... This paper concerns a problem which is basic to perception: the integration of perceptual information into a coherent description of the world. In this paper we present perception as a process of dynamically maintaining a model of the local external environment. Fusion of perceptual information is a ..."
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Cited by 40 (7 self)
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This paper concerns a problem which is basic to perception: the integration of perceptual information into a coherent description of the world. In this paper we present perception as a process of dynamically maintaining a model of the local external environment. Fusion of perceptual information is at the heart of this process.
On Unscented Kalman Filtering for State Estimation of ContinuousTime Nonlinear Systems
, 2007
"... This article considers the application of the unscented Kalman filter (UKF) to continuoustime filtering problems, where both the state and measurement processes are modeled as stochastic differential equations. The mean and covariance differential equations which result in the continuoustime lim ..."
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Cited by 38 (8 self)
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This article considers the application of the unscented Kalman filter (UKF) to continuoustime filtering problems, where both the state and measurement processes are modeled as stochastic differential equations. The mean and covariance differential equations which result in the continuoustime limit of the UKF are derived. The continuousdiscrete unscented Kalman filter is derived as a special case of the continuoustime filter, when the continuoustime prediction equations are combined with the update step of the discretetime unscented Kalman filter. The filter equations are also transformed into sigmapoint differential equations, which can be interpreted as matrix square root versions of the filter equations.
Unscented RauchTungStriebel Smoother
"... This article considers the application of the unscented transform to optimal smoothing of nonlinear state space models. In this article, a new RauchTungStriebel type form of the fixedinterval unscented Kalman smoother is derived. The new smoother differs from the previously proposed twofilter f ..."
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Cited by 36 (3 self)
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This article considers the application of the unscented transform to optimal smoothing of nonlinear state space models. In this article, a new RauchTungStriebel type form of the fixedinterval unscented Kalman smoother is derived. The new smoother differs from the previously proposed twofilter formulation based unscented Kalman smoother in the sense that it is not based on running two independent filters forward and backward in time. Instead, a separate backward smoothing pass is used, which recursively computes corrections to the forward filtering result. The smoother equations are derived as approximations to the formal Bayesian optimal smoothing equations. The performance of the new smoother is demonstrated with a simulation.
Tidal Flow Forecasting using Reduced Rank Square Root Filters
 Hydraul
, 1996
"... The Kalman filter algorithm can be used for many data assimilation problems. For large systems, that arise from discretizing partial differential equations, the standard algorithm has huge computational and storage requirements. This makes direct use infeasible for many applications. In addition num ..."
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Cited by 36 (2 self)
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The Kalman filter algorithm can be used for many data assimilation problems. For large systems, that arise from discretizing partial differential equations, the standard algorithm has huge computational and storage requirements. This makes direct use infeasible for many applications. In addition numerical difficulties may arise if due to finite precision computations or approximations of the error covariance the requirement that the error covariance should be positive semidefinite is violated. In this paper an approximation to the Kalman filter algorithm is suggested that solves these problems for many applications. The algorithm is based on a reduced rank approximation of the error covariance using a square root factorization. The use of the factorization ensures that the error covariance matrix remains positive semidefinite at all times, while the smaller rank reduces the number of computations and storage requirements. The number of computations and storage required depend on the ...
Little Ben: The Ben Franklin Racing Team’s Entry in the 2007 DARPA Urban Challenge
, 2008
"... paper describes “Little Ben, ” an autonomous ground vehicle constructed by the Ben Franklin Racing Team for the 2007 DARPA Urban Challenge in under a year and for less than $250,000. The sensing, planning, navigation, and actuation systems for Little Ben were carefully designed to meet the performan ..."
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Cited by 35 (4 self)
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paper describes “Little Ben, ” an autonomous ground vehicle constructed by the Ben Franklin Racing Team for the 2007 DARPA Urban Challenge in under a year and for less than $250,000. The sensing, planning, navigation, and actuation systems for Little Ben were carefully designed to meet the performance demands required of an autonomous vehicle traveling in an uncertain urban environment. We incorporated an array of a global positioning system (GPS)/inertial navigation system, LIDARs, and stereo cameras to provide timely information about the surrounding environment at the appropriate ranges. This sensor information was integrated into a dynamic map that could robustly handle GPS dropouts and errors. Our planning algorithms consisted of a highlevel mission planner that used information from the provided route network definition and mission data files to select routes, whereas the lower level planner used the latest dynamic map information to optimize a feasible trajectory to the next waypoint. The vehicle was actuated by a costbased controller that efficiently handled steering, throttle, and braking maneuvers in both forward and reverse directions. Our software modules were integrated within a hierarchical architecture that allowed rapid development and testing of the system performance. The resulting vehicle was one of six to successfully finish the Urban Challenge.
Detection and tracking of objects in underwater video
 In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR ’04). IEEE Computer Society
, 2004
"... For oceanographic research, remotely operated underwater vehicles (ROVs) routinely record several hours of video material each day. Manual processing of such large amounts of video has become a major bottleneck for scientific research based on this data. We have developed an automated system that de ..."
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Cited by 34 (2 self)
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For oceanographic research, remotely operated underwater vehicles (ROVs) routinely record several hours of video material each day. Manual processing of such large amounts of video has become a major bottleneck for scientific research based on this data. We have developed an automated system that detects and tracks objects that are of potential interest for human video annotators. By preselecting salient targets for track initiation using a selective attention algorithm, we reduce the complexity of multitarget tracking, in particular of the assignment problem. Detection of lowcontrast translucent targets is difficult due to variable lighting conditions and the presence of ubiquitous noise from highcontrast organic debris (“marine snow”) particles. We describe the methods we developed to overcome these issues and report our results of processing ROV video data. 1.