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A metacognitive radar switches between two modes of cognition— one mode to achieve a high-quality estimate of targets, and the other mode to hide its utility function (plan). To achieve high-quality es- timates of targets, a cognitive radar performs a constrained utility maximization to adapt its sensing mode in response to a changing target environment. If an adversary can estimate the utility function of a cognitive radar, it can determine the radar’s sensing strategy and mitigate the radar performance via electronic countermeasures (ECM). This article discusses a metacognitive radar that switches between two modes of cognition: achieving satisfactory estimates of a target while hiding its strategy from an adversary that detects cognition. The radar does so by transmitting purposefully designed suboptimal responses to spoof the adversary’s Neyman–Pearson de- tector. We provide theoretical guarantees by ensuring that the Type-I error probability of the adversary’s detector exceeds a predefined level for a specified tolerance on the radar’s performance loss. We illustrate our cognition-masking scheme via numerical examples in- volving waveform adaptation and beam allocation. We show that small purposeful deviations from the optimal emission confuse the adversary by significant amounts, thereby masking the radar’s cognition. Our approach uses ideas from revealed preference in microeconomics and adversarial inverse reinforcement learning. Our proposed algorithms provide a principled approach for system-level electronic counter- countermeasures to hide the radar’s strategy from an adversary. We also provide performance bounds for our cognition-masking scheme when the adversary has misspecified measurements of the radar’s response.
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This content will become publicly available on February 1, 2026
Fisher Information Approach for Masking the Sensing Plan: Applications in Multifunction Radars
How to design a Markov decision process (MDP)-based radar controller that makes small sacrifices in performance to mask its sensing plan from an adversary? The radar controller purposefully minimizes the Fisher information of its emissions so that an adversary cannot identify the controller’s model parameters accurately. Unlike classical open-loop statistical inference, where the Fisher information serves as a lower bound for the achievable covariance, this article employs the Fisher information as a design constraint for a closed-loop radar controller to mask its sensing plan. We analytically derive a closed-form expression for the determinant of the Fisher informa- tion matrix (FIM) pertaining to the parameters of the MDP-based controller. Subsequently, we formulate an MDP, which is regularized by the determinant of the adversary’s FIM. This results in the per- turbations to the total cost of operation, state–action costs, and the transition matrix. In addition, we propose a maximum-entropy-based convex lower bound for the FIM-constrained MDP, whose solution can serve as an initialization point for the proposed nonlinear optimization problem. This convex lower bound aligns with existing work that employs the same maximum entropy criteria to mask the sensing plan. Numerical results show that the introduction of minor perturbations to the MDP’s state–action costs, transition kernel, and the total operation cost can reduce the Fisher information of the emissions. Consequently, this reduction amplifies the variability in policy and transition kernel estimation errors, thwarting the adversary’s accuracy in estimating the controller’s sensing plan. We demonstrate this by comparing the error in the estimate of the transition kernel under different criteria: the FIM criteria, the maximum entropy criteria, a sensing plan where actions are chosen uniformly, and the unmasked sensing plan.
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- Award ID(s):
- 2312198
- PAR ID:
- 10608003
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Aerospace and Electronic Systems
- Volume:
- 61
- Issue:
- 1
- ISSN:
- 0018-9251
- Page Range / eLocation ID:
- 233 to 249
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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