Strategy Concealment Using Multi-Radar Data Fusion in Metacognitive Radars

This paper investigates a metacognitive radar scenario in which both radars and the adversary target possess cognitive capabilities, and target can infer radar's strategies defined by utility functions. In such environments, disruption of adversary cognition is achieved through smart interference design and purposeful slight variations in radar performance to hinder the target's ability to accurately infer radar operational strategies. In this work, we consider a multimetacognitive radar scenario in which multiple radars are trying to track an adversary target. The adversary has the capability to learn the individual radar's utility function using Afriat's theorembased approach. After estimating the utility function of the radar, the adversary subsequently modify its probes to reduce the utility function of each radar. In response to this, the multiple radars collaborate through a fusion center (FC), which performs weighted utility maximization. The proposed collaborative utility maximization approach hides the individual radar strategies from the adversary, which is unaware of the FC. Simulations demonstrate that the collaborative strategy effectively masks the utility function, preventing the adversary target from accurately estimating it.