.png){kind=icon}
.png){kind=icon}
Return
Markov Chain Approximation Algorithm for Event-Based State Estimation
.png){kind=icon}
PRE
AI
DOI：10.1109/TCST.2014.2349971.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This brief presents a general framework for the continuous-time nonlinear event-based state estimation problem. Using the information from observations made by event-based sampling, the goal of the event-based estimation problem is to estimate the state of stochastic differential equations which represent the uncertain system dynamics. This problem is challenging because measurements are taken only if some events happen rather than with a fixed sampling interval. In this brief, a theoretical solution for the event-based state estimation problem is derived and a numerical algorithm based on Markov chain approximation is proposed. The proposed algorithm for the event-based state estimation is demonstrated with an illustrative example.
Keywords:
Event-based estimation
event-triggered sampling
grid-based method
Markov chain approximation
stochastic differential equations (SDEs)
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.9
Papers:
4.9K
Citations:
1.7W
