Prediction and Convergence Calculations using Rust-based NAlgebra GLM

Prediction and convergence are techniques used to reduce the network traffic between multiple distributed simulation applications that individually maintain a representation of a virtual "world" that includes moving entities. Prediction (often using dead reckoning algorithms) is an approach to estimate the position and orientation of "remote" entities hosted and/or managed by other simulation applications executing within the distributed system. Estimates are made (i.e., calculated) using previously received data, such as velocity and acceleration. As new data is received, a convergence algorithm is often used to update the remote entity's position and orientation within the represented world. The term "convergence" is often times referred to as "blending" or "smoothing" as its goal is to avoid visually obvious disjointed "jumps" in movement as updates are received. This work implements the dead reckoning estimation algorithms defined in the IEEE standard for Distributed Interactive Simulation (DIS) in software using the Rust programming language and the NAlgebra GLM package (i.e., crate/library). It also implements a simple convergence algorithm to move entities to their correct locations and orientations. This work is part of a larger design effort to prototype a DIS-compatible interoperability network interface, organized using an EntityComponent-System (ECS).