An enhanced dead reckoning algorithm with hybrid extrapolation models (AisaSim 2016)

Abstract

The traditional Dead Reckoning algorithm predicts the future motion state based on a determined polynomial predictor, and the forecasting performance would vary with different types of motion entities. This paper proposes an enhanced dead reckoning algorithm based on hybrid extrapolation models, which can be used to reduce the communication in a distributed interactive simulation. The proposed algorithm perform extrapolation using a number of candidate predictors. Its idea is based on the assumption that a complex trajectory can be decomposed into several simple trajectories. The experimental evaluations show that the enhanced Dead Reckoning algorithm provides better performance in correction data reduction and accurate estimation.