The Experts below are selected from a list of 99660 Experts worldwide ranked by ideXlab platform
B A White - One of the best experts on this subject based on the ideXlab platform.
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robust localisation using data fusion via integration of covariance intersection and interval analysis
International Conference on Control Automation and Systems, 2007Co-Authors: Samuel Lazarus, Antonios Tsourdos, Rafal Zbikowski, A Nabil, B A WhiteAbstract:The problem considered here is that of robot navigation and localisation using an extended Kalman filter, interval analysis and covariance intersection. There are various approaches to the problem, but here focus is on an approach which can Guarantee Performance of sensor based navigation. The Guaranteed Performance is quantified by explicit bounds of position estimate of a mobile robot. The focus here is to achieve data fusion for the robots with low cost sensors by forming an intelligent sensor system which can provide mathematically provable Performance Guarantees that are achievable in practice. This can be accomplished by combining the sensors measurements and processing these measurements with data fusion algorithms. The algorithms are complementary in the sense that they compensate for one another's limitations, so that the resulting Performance of the sensor system is better than of its individual components.
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robot localisation and mapping using data fusion via integration of covariance intersection and interval analysis for a partially known map
European Control Conference, 2007Co-Authors: Samuel Lazarus, Antonios Tsourdos, Rafal Zbikowski, A Nabil, I Ashokaraj, P Silson, B A WhiteAbstract:The problem considered here is that of robot navigation, localisation, and mapping using an extended Kalman filter, interval analysis and covariance intersection for a partially known environment. The map is known partially in the sense that the obstacles and the land-marks are partially known. There are various approaches to the problem, but here focus is on an approach which can Guarantee Performance of sensor based navigation and mapping. The Guaranteed Performance is quantified by explicit bounds of position estimate of a mobile robot and to build the environmental map of the surroundings. The mobile robots generally carry dead reckoning sensors such as wheel encoders and inertial sensors (INS), such as accelerometers, gyroscopes, to measure acceleration and angle rate, while obstacle detection and map-making is done with time-of-flight ultrasonic sensors. Most of these sensors give overlapping or complementary information, which offers scope for exploiting data fusion. The purpose here is to achieve data fusion for the robots with low cost sensors by forming an intelligent sensor system. This is accomplished by combining the sensors' measurements and processing these measurements with data fusion algorithms. The algorithms are complementary in the sense that they compensate for each other's limitations, so that the resulting Performance of the sensor system is better than of its individual components.
Samuel Lazarus - One of the best experts on this subject based on the ideXlab platform.
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robust localisation using data fusion via integration of covariance intersection and interval analysis
International Conference on Control Automation and Systems, 2007Co-Authors: Samuel Lazarus, Antonios Tsourdos, Rafal Zbikowski, A Nabil, B A WhiteAbstract:The problem considered here is that of robot navigation and localisation using an extended Kalman filter, interval analysis and covariance intersection. There are various approaches to the problem, but here focus is on an approach which can Guarantee Performance of sensor based navigation. The Guaranteed Performance is quantified by explicit bounds of position estimate of a mobile robot. The focus here is to achieve data fusion for the robots with low cost sensors by forming an intelligent sensor system which can provide mathematically provable Performance Guarantees that are achievable in practice. This can be accomplished by combining the sensors measurements and processing these measurements with data fusion algorithms. The algorithms are complementary in the sense that they compensate for one another's limitations, so that the resulting Performance of the sensor system is better than of its individual components.
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robot localisation and mapping using data fusion via integration of covariance intersection and interval analysis for a partially known map
European Control Conference, 2007Co-Authors: Samuel Lazarus, Antonios Tsourdos, Rafal Zbikowski, A Nabil, I Ashokaraj, P Silson, B A WhiteAbstract:The problem considered here is that of robot navigation, localisation, and mapping using an extended Kalman filter, interval analysis and covariance intersection for a partially known environment. The map is known partially in the sense that the obstacles and the land-marks are partially known. There are various approaches to the problem, but here focus is on an approach which can Guarantee Performance of sensor based navigation and mapping. The Guaranteed Performance is quantified by explicit bounds of position estimate of a mobile robot and to build the environmental map of the surroundings. The mobile robots generally carry dead reckoning sensors such as wheel encoders and inertial sensors (INS), such as accelerometers, gyroscopes, to measure acceleration and angle rate, while obstacle detection and map-making is done with time-of-flight ultrasonic sensors. Most of these sensors give overlapping or complementary information, which offers scope for exploiting data fusion. The purpose here is to achieve data fusion for the robots with low cost sensors by forming an intelligent sensor system. This is accomplished by combining the sensors' measurements and processing these measurements with data fusion algorithms. The algorithms are complementary in the sense that they compensate for each other's limitations, so that the resulting Performance of the sensor system is better than of its individual components.
Cunhui Zhang - One of the best experts on this subject based on the ideXlab platform.
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sparse matrix inversion with scaled lasso
Journal of Machine Learning Research, 2013Co-Authors: Tingni Sun, Cunhui ZhangAbstract:We propose a new method of learning a sparse nonnegative-definite target matrix. Our primary example of the target matrix is the inverse of a population covariance or correlation matrix. The algorithm first estimates each column of the target matrix by the scaled Lasso and then adjusts the matrix estimator to be symmetric. The penalty level of the scaled Lasso for each column is completely determined by data via convex minimization, without using cross-validation. We prove that this scaled Lasso method Guarantees the fastest proven rate of convergence in the spectrum norm under conditions of weaker form than those in the existing analyses of other l1 regularized algorithms, and has faster Guaranteed rate of convergence when the ratio of the l1 and spectrum norms of the target inverse matrix diverges to infinity. A simulation study demonstrates the computational feasibility and superb Performance of the proposed method. Our analysis also provides new Performance bounds for the Lasso and scaled Lasso to Guarantee higher concentration of the error at a smaller threshold level than previous analyses, and to allow the use of the union bound in column-by-column applications of the scaled Lasso without an adjustment of the penalty level. In addition, the least squares estimation after the scaled Lasso selection is considered and proven to Guarantee Performance bounds similar to that of the scaled Lasso.
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sparse matrix inversion with scaled lasso
arXiv: Statistics Theory, 2012Co-Authors: Tingni Sun, Cunhui ZhangAbstract:We propose a new method of learning a sparse nonnegative-definite target matrix. Our primary example of the target matrix is the inverse of a population covariance or correlation matrix. The algorithm first estimates each column of the target matrix by the scaled Lasso and then adjusts the matrix estimator to be symmetric. The penalty level of the scaled Lasso for each column is completely determined by data via convex minimization, without using cross-validation. We prove that this scaled Lasso method Guarantees the fastest proven rate of convergence in the spectrum norm under conditions of weaker form than those in the existing analyses of other $\ell_1$ regularized algorithms, and has faster Guaranteed rate of convergence when the ratio of the $\ell_1$ and spectrum norms of the target inverse matrix diverges to infinity. A simulation study demonstrates the computational feasibility and superb Performance of the proposed method. Our analysis also provides new Performance bounds for the Lasso and scaled Lasso to Guarantee higher concentration of the error at a smaller threshold level than previous analyses, and to allow the use of the union bound in column-by-column applications of the scaled Lasso without an adjustment of the penalty level. In addition, the least squares estimation after the scaled Lasso selection is considered and proven to Guarantee Performance bounds similar to that of the scaled Lasso.
Antonios Tsourdos - One of the best experts on this subject based on the ideXlab platform.
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robust localisation using data fusion via integration of covariance intersection and interval analysis
International Conference on Control Automation and Systems, 2007Co-Authors: Samuel Lazarus, Antonios Tsourdos, Rafal Zbikowski, A Nabil, B A WhiteAbstract:The problem considered here is that of robot navigation and localisation using an extended Kalman filter, interval analysis and covariance intersection. There are various approaches to the problem, but here focus is on an approach which can Guarantee Performance of sensor based navigation. The Guaranteed Performance is quantified by explicit bounds of position estimate of a mobile robot. The focus here is to achieve data fusion for the robots with low cost sensors by forming an intelligent sensor system which can provide mathematically provable Performance Guarantees that are achievable in practice. This can be accomplished by combining the sensors measurements and processing these measurements with data fusion algorithms. The algorithms are complementary in the sense that they compensate for one another's limitations, so that the resulting Performance of the sensor system is better than of its individual components.
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robot localisation and mapping using data fusion via integration of covariance intersection and interval analysis for a partially known map
European Control Conference, 2007Co-Authors: Samuel Lazarus, Antonios Tsourdos, Rafal Zbikowski, A Nabil, I Ashokaraj, P Silson, B A WhiteAbstract:The problem considered here is that of robot navigation, localisation, and mapping using an extended Kalman filter, interval analysis and covariance intersection for a partially known environment. The map is known partially in the sense that the obstacles and the land-marks are partially known. There are various approaches to the problem, but here focus is on an approach which can Guarantee Performance of sensor based navigation and mapping. The Guaranteed Performance is quantified by explicit bounds of position estimate of a mobile robot and to build the environmental map of the surroundings. The mobile robots generally carry dead reckoning sensors such as wheel encoders and inertial sensors (INS), such as accelerometers, gyroscopes, to measure acceleration and angle rate, while obstacle detection and map-making is done with time-of-flight ultrasonic sensors. Most of these sensors give overlapping or complementary information, which offers scope for exploiting data fusion. The purpose here is to achieve data fusion for the robots with low cost sensors by forming an intelligent sensor system. This is accomplished by combining the sensors' measurements and processing these measurements with data fusion algorithms. The algorithms are complementary in the sense that they compensate for each other's limitations, so that the resulting Performance of the sensor system is better than of its individual components.
Rafal Zbikowski - One of the best experts on this subject based on the ideXlab platform.
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robust localisation using data fusion via integration of covariance intersection and interval analysis
International Conference on Control Automation and Systems, 2007Co-Authors: Samuel Lazarus, Antonios Tsourdos, Rafal Zbikowski, A Nabil, B A WhiteAbstract:The problem considered here is that of robot navigation and localisation using an extended Kalman filter, interval analysis and covariance intersection. There are various approaches to the problem, but here focus is on an approach which can Guarantee Performance of sensor based navigation. The Guaranteed Performance is quantified by explicit bounds of position estimate of a mobile robot. The focus here is to achieve data fusion for the robots with low cost sensors by forming an intelligent sensor system which can provide mathematically provable Performance Guarantees that are achievable in practice. This can be accomplished by combining the sensors measurements and processing these measurements with data fusion algorithms. The algorithms are complementary in the sense that they compensate for one another's limitations, so that the resulting Performance of the sensor system is better than of its individual components.
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robot localisation and mapping using data fusion via integration of covariance intersection and interval analysis for a partially known map
European Control Conference, 2007Co-Authors: Samuel Lazarus, Antonios Tsourdos, Rafal Zbikowski, A Nabil, I Ashokaraj, P Silson, B A WhiteAbstract:The problem considered here is that of robot navigation, localisation, and mapping using an extended Kalman filter, interval analysis and covariance intersection for a partially known environment. The map is known partially in the sense that the obstacles and the land-marks are partially known. There are various approaches to the problem, but here focus is on an approach which can Guarantee Performance of sensor based navigation and mapping. The Guaranteed Performance is quantified by explicit bounds of position estimate of a mobile robot and to build the environmental map of the surroundings. The mobile robots generally carry dead reckoning sensors such as wheel encoders and inertial sensors (INS), such as accelerometers, gyroscopes, to measure acceleration and angle rate, while obstacle detection and map-making is done with time-of-flight ultrasonic sensors. Most of these sensors give overlapping or complementary information, which offers scope for exploiting data fusion. The purpose here is to achieve data fusion for the robots with low cost sensors by forming an intelligent sensor system. This is accomplished by combining the sensors' measurements and processing these measurements with data fusion algorithms. The algorithms are complementary in the sense that they compensate for each other's limitations, so that the resulting Performance of the sensor system is better than of its individual components.