Extensions of Hidden Markov Models for supporting instructors in the analysis of training operations in an Unmanned Aircraft System

Abstract

The increasing use of Unmanned Aircraft Systems has not been met with appropriate integration of training science. Most of the tasks of evaluation and analysis carried out by an instructor during the debriefing of a training session are still performed rudimentarily and individually for each operator, due to the current lack of methods and tools capable of doing it automatically on a large scale. This work is focused on providing intelligent and automated methods to training operations in a UAS by supporting instructors in some debriefing tasks, such as the extraction of behavioural patterns. In this regard, the current methods based on Hidden Markov Models (HMMs) are used to create predictive models of the operator’s behaviour. These methods have been extended in two different ways: first, the use of Multichannel HMMs is proposed in order to enrich the meaningfulness of the model states with the usage of parallel sources of information from the mission logs; secondly, the inner modelling limitations of HMMs are considered, and based on this, the applicability of a more flexible approach based on high order Double Chain Markov Models (DCMMs) is studied. In order to demonstrate the effectiveness of each of the proposed approaches, several experiments have been carried out in a lightweight simulation environment, with inexperienced operators.