Identifying Coordination in a Cognitive Radar Network -- A Multi-Objective Inverse Reinforcement Learning Approach
This addresses the challenge for targets in radar networks to infer coordination strategies, with potential applications in defense and surveillance, though it appears incremental as it adapts existing economic methods to a specific domain.
The paper tackles the problem of detecting coordination among cognitive radars by analyzing intercepted emissions, using a multi-objective inverse reinforcement learning approach to identify Pareto optimal behavior and reconstruct utility functions from finite data.
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.