Meta-Cognition. An Inverse-Inverse Reinforcement Learning Approach for Cognitive Radars
- Resource Type
- Conference
- Authors
- Pattanayak, Kunal; Krishnamurthy, Vikram; Berry, Christopher
- Source
- 2022 25th International Conference on Information Fusion (FUSION) Information Fusion (FUSION), 2022 25th International Conference on. :01-08 Jul, 2022
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Differential privacy
Error probability
Riccati equations
Radar detection
Reinforcement learning
Cognitive radar
Eigenvalues and eigenfunctions
Cognitive Radar
Revealed Preference
Adversarial Inverse Reinforcement Learning
Electronic Counter Countermeasures' Kalman Filter
- Language
This paper considers meta-cognitive radars in an adversarial setting. A cognitive radar optimally adapts its wave-form (response) in response to maneuvers (probes) of a possibly adversarial moving target. A meta-cognitive radar is aware of the adversarial nature of the target and seeks to mitigate the adversarial target. How should the meta-cognitive radar choose its responses to sufficiently confuse the adversary trying to estimate the radar's utility function? This paper abstracts the radar's meta-cognition problem in terms of the spectra (eigenvalues) of the state and observation noise covariance matrices, and embeds the algebraic Riccati equation into an economics-based utility maximization setup. This adversarial target is an inverse reinforcement learner. By observing a noisy sequence of radar's responses (waveforms), the adversarial target uses a statistical hypothesis test to detect if the radar is a utility maximizer. In turn, the meta-cognitive radar deliberately chooses sub-optimal responses that increasing its Type-I error probability of the adversary's detector. We call this counter-adversarial step taken by the meta-cognitive radar as inverse inverse reinforcement learning (I-IRL). We illustrate the meta-cognition results of this paper via simple numerical examples. Our approach for meta-cognition in this paper is based on revealed preference theory in micro-economics and inspired by results in differential privacy and adversarial obfuscation in machine learning.