Monte Carlo Evaluation of an Iterative Technique for the Design of Observer Field Tests

摘要

In field tests to compare the observability of combat vehicles, the test designer must select the optimum number of observation opportunities in order to balance collecting enough data to draw valid conclusions against the high cost of supporting vehicles and personnel at a test site. The test designer, however, generally lacks key parameters for the efficient design of the test Namely, the designer lacks the detection probabilities of the vehicles at each range. The standard deviation of the difference in detection probability depends upon the detection probability itself. Therefore, the test designer must select the number of observations for each range based upon the conservative assumption that the probabilities are near 50%, the probability for the maximum standard deviation. In a previous paper, I presented an iterative technique of test design in order to improve the efficiency of observability tests. in this paper, I present the results of a Monte Carlo evaluation of this iterative technique.