This article considers three interrelated adversarial inference problems involving cognitive radars. We first discuss inverse tracking of the radar to estimate the adversary's estimate of us based on the radar's actions and calibrate the radar's sensing accuracy. Second, using revealed preference from microeconomics, we formulate a nonparametric test to identify if the cognitive radar is a constrained utility maximizer with signal processing constraints. We consider two radar functionalities, namely, beam allocation and waveform design, with respect to which the cognitive radar is assumed to maximize its utility and construct a set-valued estimator for the radar's utility function. Finally, we discuss how to engineer interference at the physical layer level to confuse the radar that forces it to change its transmit waveform. The levels of abstraction range from smart interference design based on Wiener filters (at the pulse/waveform level), inverse Kalman filters at the tracking level, and revealed preferences for identifying utility maximization at the systems level.

中文翻译：

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对抗性雷达推理：逆向跟踪、识别认知和设计智能干扰

本文考虑了涉及认知雷达的三个相互关联的对抗性推理问题。我们首先讨论雷达的逆向跟踪，以根据雷达的动作来估计对手对我们的估计，并校准雷达的感知精度。其次，使用微观经济学的显示偏好，我们制定了一个非参数检验来确定认知雷达是否是具有信号处理约束的约束效用最大化器。我们考虑两种雷达功能，即波束分配和波形设计，关于这两个功能，认知雷达被假定为最大化其效用，并为雷达的效用函数构建一个设定值估计器。最后，我们讨论如何在物理层级别设计干扰以混淆雷达，迫使其改变其发射波形。