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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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System-level investigation of cognitive adaptation in incident management.
My dissertation research to date has focused on understanding how incident management teams (IMTs), hastily formed multidisciplinary multiteam systems, cognitively function together as adaptive, joint cognitive systems-of-systems embedded in complex sociotechnical systems. Catastrophic disasters such as Hurricane Harvey highlight the importance of collective efforts for adaptive incident management. Team cognition has emerged as a coordinating mechanism in safety-critical disciplines; however, little is known about cognition in IMTs. Through a scoping review of existing definitions, I proposed an expanded definition that deliberately takes into account IMT’s unique contextual characteristics, based on three premises: cognition in IMTs (1) manifests as interactions among humans, teams, and technologies at multiple levels of multiteam systems, (2) aims to achieve the system-level cognitive goals of perceiving (P), diagnosing, (D), and adapting (A) to information, and (3) serves as an open communication platform for adaptive coordination.Then, I operationalized our proposed definition in a simulated environment as an initial attempt to model IMTs’ system-level cognition. Based on several observations of IMTs’ naturalistic interactive behaviors under different types of disaster scenarios, I proposed a model that can capture how IMTs as joint cognitive systems (or systems-of-systems) perceive (P), diagnose, (D), and adapt (A) to information, i.e., perceive, diagnose, adapt (P, D, A) model. With an emphasis on system-level cognitive goals that applies to multiple units of analysis (e.g., individuals, dyads, teams, and multiteam systems), I could gain an understanding of system-level cognitive adaptation in incident management. Using the P, D, A model as a base platform, I expect to discuss resilience as cognitive adaptation processes along with its implications on human information processing and joint cognitive systems theories.I became a Ph.D. candidate after successfully proposing my dissertation research in last June. After completing data collection and processing, I am currently working on data analysis and manuscript preparation. As a part of NSF-funded project (NSF EArly-concept Grant for Exploratory Research, #1724676), I believe my dissertation work has a potential to practically impact scenario-based training practices of incident management, and thereby lead to a more rapid and better coordinated decision-making in saving lives and infrastructures.
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- Award ID(s):
- 1724676
- PAR ID:
- 10135711
- Date Published:
- Journal Name:
- Resilience Engineering Association (REA) Symposium
- Volume:
- 2019
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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