Identifying Coordination in a Cognitive Radar Network - A Multi-Objective Inverse Reinforcement Learning Approach
- Resource Type
- Conference
- Authors
- Snow, Luke; Krishnamurthy, Vikram; Sadler, Brian M.
- Source
- ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2023 - 2023 IEEE International Conference on. :1-5 Jun, 2023
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Target tracking
Radar detection
Reinforcement learning
Signal processing
Pareto optimization
Cognitive radar
Radar tracking
Cognitive Radar
Multi-Objective Inverse Reinforcement Learning
Revealed Preferences
- Language
- ISSN
- 2379-190X
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 multiobjective 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.