Kalman filter based dead reckoning for minimizing network traffic for 3D on-line games

Online 3D games requires efficient user interaction support over network, and the networking support is usually implemented using network game engine. The network game engine should minimize the network delay and mitigate the network traffic congestion. To minimize the network traffic between networking game users, dead reckoning technique is used. Each game entity uses the algorithm to estimates its movement and other entities’ movement, thereby, each entity can minimize the transmission of its updated information (position, velocity, etc) to other entities. To improve the prediction accuracy of dead reckoning algorithm, we propose the Kalman filter based approach. To show real demonstration, we improved the dead reckoning algorithm that is optimized for a popular networking game (BZFlag) using Kalman filter, and showed that prediction accuracy is improved and network traffic is reduced.