Awning
发表于 2025-3-25 05:48:18
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cavity
发表于 2025-3-25 07:41:14
A Framework of Finite-Model Kalman Filter with Case Study: MVDP-FMKF Algorithmrk well or even diverge in the presence of large model uncertainty. To resolve this problem occurred frequently in practical applications, where it is expensive to have large number of high-cost experiments or even impossible to obtain the exact system model, motivated by our previous pioneering wor
湿润
发表于 2025-3-25 13:12:42
Kalman Filters for Continuous Parametric Uncertain Systems Kalman filter is highly restricted by strict a priori requirements on the information of dynamic model and statistical information of process noises. In practice, the covariance matrix of process noise is almost impossible to be directly determined a priori due to the intrinsic coupling of process
刀锋
发表于 2025-3-25 16:29:19
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影响带来
发表于 2025-3-25 21:18:31
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–scent
发表于 2025-3-26 01:16:23
CKF-Based State Estimation of Nonlinear System by Fusion of Multirate Multisensor Unreliable Measurent sampling rates, and the observations are randomly unreliable. Based on online checking of the reliability of the measurements, the cubature Kalman filter (CKF) is improved, which has better robustness and stability. Based on the improved CKF, an effective state estimation algorithm is presented,
挖掘
发表于 2025-3-26 06:38:48
Decentralized Adaptive Filtering for Multi-agent Systems with Uncertain Couplings problem is challenging due to the mutual dependency of state estimation and coupling estimation. First, the problem is divided into four typical types based on the origin of coupling relations and linearity of the agent dynamics. Then models of the four types are given, and the corresponding decent
多产鱼
发表于 2025-3-26 12:17:23
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木质
发表于 2025-3-26 16:03:12
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Arctic
发表于 2025-3-26 19:38:27
Optimal Estimation for Multirate Systems with Unreliable Measurements and Correlated Noisegly, considerable research attention has been devoted to state estimation techniques over sensor networks, not only due to a large number of potential applications but also because they provide more information than traditional communication systems with a single sensor.