Simulation Fidelity through an Adaptive Pilot Model

Simulation Fidelity

The level of fidelity of Flight Simulators, or, more generally Synthetic Training Devices (STD), determines their fitness for purpose and is quantified in documents like JAR-STD-1H in terms of performance criteria for the individual components, e.g. the motion/visual/sound systems, the mathematical model. 

Component fidelity is important but the standards also require piloted assessment of the integrated system with typical mission sorties flown covering the training aspects for which the system will be used.  Subjective opinion here is important too because it reflects the value that an experienced pilot places on the level of realism.  Quantifying overall simulation fidelity is more difficult however, but is equally important because, arguably, component or sub-system fidelity can only be properly related to fitness for purpose if connected by measure to the whole.

Attempts to quantify overall simulation fidelity within the framework of handling qualities engineering have been presented in a number of forms in recent years.  Hess and colleagues have developed an approach based on pilot-aircraft modelling and introduced the handling qualities sensitivity function as the basis of a quality metric.  McCallum et al propose the use of the ADS-33 performance standards for deriving metrics.  Within the JSHIP project, Wilkinson and Advani, and Roscoe and Thompson present an approach using comparative measures of performance and control activity, correlated with handling qualities ratings given for the same tasks flown in simulation and flight.  In all these approaches, the philosophy has been to develop a rational and systematic approach to identifying differences between tasks performed in simulation and flight, hence directing attention to simulation deficiencies.  While JAR-STD 1H is directed at the training community, fidelity criteria are equally applicable to simulation in design, research and development.  In these areas, flight simulation can be a primary source of data from which knowledge is derived, decisions are made and significant resources committed.