论文标题
在认知雷达网络中识别协调 - 一种多目标逆增强学习方法
Identifying Coordination in a Cognitive Radar Network -- A Multi-Objective Inverse Reinforcement Learning Approach
论文作者
论文摘要
考虑通过认知雷达网络跟踪的目标。如果目标可以拦截某些雷达网络排放，它如何检测雷达之间的配位？通过“协调”，我们的意思是，雷达排放满足了每个雷达效用的多目标优化的帕累托最优性。本文提供了一种新型的多目标逆增强学习方法，既可以检测这种帕累托最佳（“协调”）行为，又可以通过雷达网络发射的有限数据集，对每个雷达的效用函数进行了后续重建。实现此目的的方法源自揭示的偏好的微观经济环境，也适用于对多目标优化系统的反检测和学习的更一般问题。
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.
