An improvement of dead reckoning algorithm using Kalman filter for minimizing network traffic of 3D on-line games

Online 3D games require efficient and fast 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 game users, a client-based prediction (dead reckoning algorithm) is used. Each game entity uses the algorithm to estimates its own movement (also other entities' movement), and when the estimation error is over threshold, the entity sends the UPDATE (including position, velocity, etc) packet to other entities. As the estimation accuracy is increased, each entity can minimize the transmission of the UPDATE packet. To improve the prediction accuracy of dead reckoning algorithm, we propose the Kalman filter based dead reckoning approach. To show real demonstration, we use a popular network game (BZFlag), and improve the game optimized dead reckoning algorithm using Kalman filter. We improve the prediction accuracy and reduce the network traffic by 12 percents.