The objective of this program is to design a computer algorithm and prototype implementation to track aircraft in a sparse reporting environment. The probability density function of the observed aircraft will be propagated between arrivals of new target reports by means of the numerical solution of a partial differential equation of evolution of a nonlinear stochastic differential equation of aircraft motion. The resulting non-Gaussian density will be combined with new reports using Bayesian methods to update estimates of aircraft position. In order to perform the tracking function in realtime, the algorithm will be designed to work in paralled computing environment. When implemented in the multiprocessor environment, the algorithm will improve the performance of a currently operational software system used for aircraft detection and tracking.
