  Consider a target being tracked by a cognitive radar network. If the target
can intercept some radar network emissions, how can it detect coordination
among the radars? By 'coordination' we mean that the radar emissions satisfy
Pareto optimality with respect to multi-objective optimization over each
radar's utility. This paper provides a novel multi-objective inverse
reinforcement learning approach which allows for both detection of such Pareto
optimal ('coordinating') behavior and subsequent reconstruction of each radar's
utility function, given a finite dataset of radar network emissions. The method
for accomplishing this is derived from the micro-economic setting of Revealed
Preferences, and also applies to more general problems of inverse detection and
learning of multi-objective optimizing systems.
