Simulation model validation based on OMLHD method

When simulation factors are numerous while real-world observed data are sparse, the issue of validating the simulation models is problematic. An extreme case was focused that limited real-world observations were available cross the factor space, and only a single replicate was obtained on per simulation factor setting. A method based on orthogonal-maximin Latin hypercube designs (OMLHD) was proposed by which the validation experiments could be well arranged across the factor space through optimal design. The p-value test and the inverse cumulative distribution function (CDF) theorem were introduced to evaluate the statistical consistency of the simulation/observation data, and combine the analysis results to make an overall validation study on the entire factor space. An example of validation of a guided missile simulation was taken, which demonstrates that the method is useful.