Results 241 to 250 of about 19,820,982 (300)

Review of State Estimation Methods for Autonomous Ground Vehicles: Perspectives on Estimation Objects, Vehicle Characteristics, and Key Algorithms. [PDF]

Sensors (Basel)
Wang X, Chen T, Wang R, Lu J, Dou G.
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Distributed Non-Fragile State Estimation for Uncertain Nonlinear Systems of Sensor Networks Subject to Sensor Nonlinearities. [PDF]

Sensors (Basel)
Tian S, Xu K, Huang F.
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A Hybrid Soft Sensor Approach Combining Partial Least-Squares Regression and an Unscented Kalman Filter for State Estimation in Bioprocesses. [PDF]

Bioengineering (Basel)
Hermann L, Kremling A.
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State estimation in an islanded hybrid solar-wind DC microgrid using Unscented Kalman Filter. [PDF]

Heliyon
HajiHeydari Varnoosfaderani N, Khorsandi A.   +1 more
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Consensus-Based Power System State Estimation Algorithm Under Collaborative Attack. [PDF]

Sensors (Basel)
Cheng Z, Chen G, Li XM, Ren H.
europepmc   +1 more source

Research on the condition monitoring method of unmanned aerial vehicle based on improved multivariate state estimation technique. [PDF]

Sci Rep
Zhou H, Zhou J, Li Y, Cai F.
europepmc   +1 more source

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State Estimation with Probability Constraints

Proceedings of the 44th IEEE Conference on Decision and Control, 2006
This paper considers a state estimation problem for a discrete-time linear system driven by a Gaussian random process. The second order statistics of the input process and state initial condition are uncertain. However, the probability that the state and input satisfy linear constraints during the estimation interval is known.
Mario A. Rotea, Carlos Lana
openaire   +2 more sources

State Estimation With Initial State Uncertainty

IEEE Transactions on Information Theory, 2008
The problem of state estimation with initial state uncertainty is approached from a statistical decision theory point of view. The initial state is regarded as deterministic and unknown. It is only known that the initial state vector belongs to a specified parameter set.
Yoav Levinbook, Tan F. Wong
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Memory-Based State-Estimation

Fundamenta Informaticae, 2008
In this paper we introduce a state-estimation method that uses a short-term memory to calculate the current state. A common way to solve state estimation problems is to use implementations of the Bayesian algorithmlike Kalman filters or particle filters. When implementing a Bayesian filter several problems can arise.
Matthias Jüngel, Heinrich Mellmann
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Reinforcement Learning State Estimator

Neural Computation, 2007
In this study, we propose a novel use of reinforcement learning for estimating hidden variables and parameters of nonlinear dynamical systems. A critical issue in hidden-state estimation is that we cannot directly observe estimation errors. However, by defining errors of observable variables as a delayed penalty, we can apply a reinforcement learning ...
Jun Morimoto, Kenji Doya
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