| Summary: | 碩士 === 國立成功大學 === 工程科學系 === 87 === Every participant on a networked multi-user virtual environment has to send his update message to others of the system, in order to keep the consistency of the scene for all the participants. As the number of users in the environment is grown, then the requirement of system bandwidth is also increased. To solve the bandwidth problem, prediction technologies are used to estimate the state of each participant on the environment locally. These prediction technologies are called “dead reckoning algorithms”. Dead reckoning algorithms predict the state of every user on the environment by using the collected nearest update messages from others. Linear Extrapolation, the dead reckoning algorithm of DIS(Distributed Interactive Simulation) and Kalman filter are usually used to predict the moving trajectory of the object on a networked multi-user virtual environment. In this thesis, grey theory is applied to do the prediction. The GM(1,1) model is used. It needs fewer history sample data and taked shorter time than the other three mentioned methods. Grey prediction is as accuracy as Kalman filter and DIS and has a smaller update message packets. It is shown that grey prediction is more suitable for dead reckoning algorithm than other prediction methods on networked multi-user virtual environments.
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