In this paper, a novel nonlinear/nonlinear model decomposition for the Sliced Gaussian Mixture Filter is presented. Based on the level of nonlin-earity of the model, the overall estimation problem is decomposed into a...
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In this paper, a novel nonlinear/nonlinear model decomposition for the Sliced Gaussian Mixture Filter is presented. Based on the level of nonlin-earity of the model, the overall estimation problem is decomposed into a "severely" nonlinear and a "slightly" nonlinear part, which are processed by different estimation techniques. To further improve the efficiency of the estimator, an adaptive state decomposition algorithm is introduced that allows decomposition according to the linearization error for nonlinear system and measurement models. Simulations show that this approach has orders of magnitude less complexity compared to other state of the art estimators, while maintaining comparable estimation errors.
This paper copes with the problem of nonlinear Bayesian state estimation. A nonlinear filter, the sliced Gaussian mixture filter (SGMF), employs linear substructures in the nonlinear measurement and prediction model i...
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ISBN:
(纸本)9780982443804
This paper copes with the problem of nonlinear Bayesian state estimation. A nonlinear filter, the sliced Gaussian mixture filter (SGMF), employs linear substructures in the nonlinear measurement and prediction model in order to simplify the estimation process. Here, a special density representation, the sliced Gaussian mixture density, is used to derive an exact solution of the Chapman-Kolmogorov equation. The sliced Gaussian mixture density is obtained by a systematic and deterministic approximation of a continuous density minimizing a certain distance measure. In contrast to previous work, improvements of the SGMF presented here include an extended system model and the processing of multi-dimensional nonlinear subspaces. As an application for the SGMF, cooperative passive target tracking, where sensors take angular measurements from a target, is considered in this paper. Finally, the performance of the proposed estimator is compared to the marginalized particle filter (MPF) in simulations.
This paper addresses the efficient state estimation for mixed linear/nonlinear dynamic systems with noisy measurements. Based on a novel density representation - sliced Gaussian mixture density - the decomposition int...
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This paper addresses the efficient state estimation for mixed linear/nonlinear dynamic systems with noisy measurements. Based on a novel density representation - sliced Gaussian mixture density - the decomposition into a (conditionally) linear and nonlinear estimation problem is derived. The systematic approximation procedure minimizing a certain distance measure allows the derivation of (close to) optimal and deterministic estimation results. This leads to high-quality representations of the measurement-conditioned density of the states and, hence, to an overall more efficient estimation process. The performance of the proposed estimator is compared to state-of-the-art estimators, like the well-known marginalized particle filter.
Image matting is important in digital image processing. The pixel pair optimization-based methods are some of the image matting methods that have distinct advantages in spatially disconnected foreground. However, they...
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Recruiting candidates who will perform well within any organization has become a challenge in the Human Resources sector. Generally, poor recruitment can negatively reflect the progression and evolution of the organis...
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