Nonlinear state and parameter estimation of spatially distributed systems
In this thesis two probabilistic model-based estimators are introduced that allow the reconstruction and identification of space-time continuous physical systems. The Sliced Gaussian Mixture Filter (SGMF) exploits linear substructures in mixed linear/nonlinear systems, and thus is well-suited for id...
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| Tác giả chính: | |
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| Định dạng: | Online |
| Ngôn ngữ: | Tiếng Anh |
| Được phát hành: |
KIT Scientific Publishing
2021
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| Những chủ đề: | |
| Truy cập trực tuyến: | 34479 |
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Những quyển sách tương tự: Nonlinear state and parameter estimation of spatially distributed systems
- State Estimation for Distributed Systems with Stochastic and Set-membership Uncertainties
- Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos
- Simultaneous Tracking and Shape Estimation of Extended Objects
- Deterministic Sampling for Nonlinear Dynamic State Estimation
- International Journal of Distributed Sensor Networks
- Physics-Based Probabilistic Motion Compensation of Elastically Deformable Objects