Since a particular algorithm’s perf

Since a particular algorithm’s performance is data-driven, JHU/APL will simulate a span of scenarios using a combinatorics approach to explore the range of data conditions that the system is expected to encounter during operations. The goal will thus be to generate performance curves or surfaces (as opposed to points) that are functions of each tuning parameter. The data quality conditions, here used as tuning parameters, are as follows: (1) number of sensors, (2) sensor noise, (3) sensor bias, (4) number of observations, (5) temporal spacing between observations, and (6) orbit regime.1 Table 1 lists each tuning parameter along with a range of values used as a discrete representation of a continuous span. If a particular scenario is described by selecting one value from each column, in a pure combination sense, the number of possible scenarios to consider is 864. Automation techniques are under development to allow timely generation of configuration files and execution of each simulation to be completed prior to the demonstration phase.Table 1. Discrete ranges for each tuning parameter.