E. A. Wan and R. Van der Merwe. The Unscented Kalman Filter. In Kalman Filtering and Neural Networks, chapter 7. Wiley, 2001. Home/Search Document Details and Download
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A Comparison of Unscented and Extended Kalman Filtering for.. - LaViola, Jr. (2003) (1 citation) (Correct)
....Jacobian matrices, the UKF uses a deterministic sampling approach to capture the mean and covariance estimates with a minimal set of sample points[9] As with the EKF, we present an algorithmic description of the UKF omitting some theoretical considerations. More details can be found in [7] 6][18]. 1 (we use the same state vector as in equation 2, we compute a collection of sigma points, stored in the columns of the L (2L 1) sigma point k 1 where L is the dimension of the state vector. In our case, L = 7 so k 1 is a 7 15 matrix. The columns of k 1 are computed by (X k 1 ) 0 ....
Wan, E. A., and R. van der Merwe. The Unscented Kalman Filter, In Kalman Filtering and Neural Networks, S. Haykin (ed.), Wiley Publishing, 2001.
A Multisine Approach for Trajectory Optimization.. - Mihaylova, De.. (2002) (Correct)
....a similar way, e.g. about the rate of change of v k , the rate of change of # k . 4 Simulation Studies This Section presents simulation results showing the performance of the multisine approach. The state estimation used throughout the present paper is based on the Unscented Kalman Filter (UKF) [3, 13]. The sigma points and their weights are calculated using the scaled Unscented Transform [14, 13] It does not require linearization, nor explicit calculation of Jacobians and Hessians and it is numerically stable due to its factorization based form. Example 1. The WMR is moving in the presence ....
....Studies This Section presents simulation results showing the performance of the multisine approach. The state estimation used throughout the present paper is based on the Unscented Kalman Filter (UKF) 3, 13] The sigma points and their weights are calculated using the scaled Unscented Transform [14, 13]. It does not require linearization, nor explicit calculation of Jacobians and Hessians and it is numerically stable due to its factorization based form. Example 1. The WMR is moving in the presence of a reference trajectory straight line in an obstaclefree environment between starting and goal ....
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E. Wan and R. van der Merwe, "The Unscented Kalman filter," in Kalman Filtering and Neural Networks (S. Haykin, ed.), Wiley Publ., 2001.
Active Sensing Of A Nonholonomic Wheeled Mobile Robot - Mihaylova, Bruyninckx, De.. (Correct)
.... . The criterion introduced in this way is a dimensionless scalar. As good are considered trajectories which at the goal configuration have the first term within the range D I . The state estimation in the present paper is carried out based on the Unscented Kalman Filter (UKF) 10] [11] for state vector estimation. The UKF is implemented in its form with an augmented state vector (a concatenation of the states and the noises) 11] The sigma points and their weights are calculated using the scaled Unscented Transform [11] The WMR and beacon models, 1) and (2) are highly ....
....the first term within the range D I . The state estimation in the present paper is carried out based on the Unscented Kalman Filter (UKF) 10] 11] for state vector estimation. The UKF is implemented in its form with an augmented state vector (a concatenation of the states and the noises) [11]. The sigma points and their weights are calculated using the scaled Unscented Transform [11] The WMR and beacon models, 1) and (2) are highly nonlinear, that motivates the use of the UKF as a filtering algorithm. It does not require linearization, nor explicit calculation of Jacobians and ....
[Article contains additional citation context not shown here]
E. Wan and R. van der Merwe, The Unscented Kalman Filter. Wiley Publishing, Ed. S. Haykin, 2001.
A Survey of Maneuvering Target Tracking: Approximation.. - Li, Jilkov (2004) (Correct)
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E. A. Wan and R. Van der Merwe. The Unscented Kalman Filter. In Kalman Filtering and Neural Networks, chapter 7. Wiley, 2001.
Unscented Kalman filtering for nonlinear - Structural Dynamics Stefano (Correct)
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E.A. Wan and R. van der Merwe. The unscented Kalman filter. In S. Haykin, editor, Kalman filtering and neural networks, pages 221--280. John Wiley & Sons, Inc., 2001.
State Inference in Variational Bayesian - Nonlinear State-Space Models (Correct)
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E. A. Wan and R. van der Merwe, "The unscented Kalman filter," in Kalman Filtering and Neural Networks (S. Haykin, ed.), pp. 221--280, New York: Wiley, 2001.
Structural Similarity-Based Object Tracking in Video.. - Loza, Mihaylova.. (2006) (Correct)
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E. Wan and R. van der Merwe. The Unscented Kalman filter. In S. Haykin, editor, Kalman Filtering and Neural Networks, chapter 7, pages 221-- 280. Wiley Publishing, Sep. 2001.
Mobility Tracking in Cellular Networks with.. - Mihaylova, Bull.. (2005) (Correct)
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E. Wan and R. van der Merwe. Kalman Filtering and Neural Networks, chapter 7. the Unscented Kalman filter 7, pages 221--280. Wiley Publishing, September 2001.
A Particle Filter for Freeway Traffic Estimation - Lyudmila Mihaylova Ren (Correct)
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E. Wan, and R. van der Merwe, The Unscented Kalman Filter, in Kalman Filtering and Neural Networks, S. Haykin, Ed., Chapter 7, pp. 221--280, Wiley Publ., 2001.
A Multisine Approach for Trajectory Optimization.. - Mihaylova, De.. (2003) (Correct)
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E. Wan, R. van der Merwe, The unscented Kalman filter, in: S. Haykin (Ed.), Kalman Filtering and Neural Networks, Wiley, New York, 2001.
The Predictive Tracking Algorithm Testing Suite: A Tool for.. - LaViola, Jr. (2002) (Correct)
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, Wan, E. A., and R. van der Merwe. The Unscented Kalman Filter, In Kalman Filtering and Neural Networks, S. Haykin (ed.), Wiley Publishing, 2001.
Proposed design for gR, a graphical models toolkit for R - Murphy (2003) (Correct)
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E. A. Wan and R. Van der Merwe. The Unscented Kalman Filter. In S. Haykin, editor, Kalman Filtering and Neural Networks. Wiley, 2001. 40
A Testbed for Studying and Choosing Predictive Tracking.. - LaViola, Jr. (2003) (Correct)
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Wan, E. A., and R. van der Merwe. The Unscented Kalman Filter, In Kalman Filtering and Neural Networks, S. Haykin (ed.), Wiley Publishing, 2001.
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