Bayesian Network Encounter Models of Manned Aircraft Behavior

The Bayesian network encounter models are a collection of MATLAB scripts that produce random samples from models of how different aircraft behave, as previously documented in MIT Lincoln Laboratory technical reports. All these models were originally developed by MIT Lincoln Laboratory. Majority of these samples are of one independent aircraft track and a single sample is insufficient for a complete synthetic encounter. Refer to the em-overview/README for model documentation. Also please refer to other software and documentation on how to fully generate an encounter.

Each manned aircraft model is a set of Bayesian Networks, a representation of a multivariate probability distribution as a directed acyclic graph. Models are trained using aircraft operational data derived from radar or other sensing system flight track data. For example, this figure illustrates the initial Bayesian network for the extended uncorrelated encounter model.