AI 中文总结
本文研究反对抗系统中的逆认知问题，针对现有逆随机滤波仅适用于线性高斯或有限状态空间模型的局限，在一般非线性非高斯框架下提出逆非线性滤波方法，以支持认知雷达等场景中的目标防御。
AI 中文摘要
近年来，在认知和反对抗系统方面取得的进展，这些系统能够推断其对手的信念，已引起了对贝叶斯视角下逆滤波的广泛研究兴趣。此类反对抗系统在自主系统与环境交互、学习相关信息并自适应地实现其目标方面具有潜在应用。一个重要例子是认知雷达-目标动力学中的逆认知问题。认知雷达实时调整其波形、波束模式和其他参数，以准确定位其目标。智能目标可以利用这些逆滤波结果来逃避检测，甚至欺骗对抗雷达。在这种背景下，认知对手或“攻击者”使用诸如卡尔曼滤波器（KF）之类的随机框架来跟踪其感兴趣的目标。然后，目标或“防御者”采用另一种逆随机滤波器来推断攻击者计算出的前向滤波器对防御者状态的估计。例如，最近开发的逆卡尔曼滤波器（I-KF）通过估计攻击者的（前向）KF估计来预测攻击者的未来步骤。现有的逆随机滤波和逆认知工作仅考虑线性高斯或有限状态空间模型，这限制了它们在许多实际工程问题中的适用性。因此，在本论文中，我们研究一般非线性非高斯系统框架下的逆滤波，并使用不同的次优方法开发逆非线性滤波器。
英文摘要
Recent advances in cognitive and counter-adversarial systems with the ability to infer their adversary's beliefs have garnered significant research interest in inverse filtering from a Bayesian perspective. Such counter-adversarial systems have potential applications where an autonomous system interacts with its environment, learns relevant information about it, and then adapts itself to achieve its goals optimally. An important example is the inverse cognition problem in cognitive radar-target dynamics. A cognitive radar adapts its waveform, beam pattern, and other parameters in real-time to accurately localize its target. An intelligent target can employ these inverse filtering results to evade detection and even deceive the adversarial radar. In this setting, a cognitive adversary or 'attacker' tracks its target of interest using a stochastic framework such as a Kalman filter (KF). The target or 'defender' then employs another inverse stochastic filter to infer the forward filter's estimates of the defender's state computed by the attacker. For instance, recently, inverse KF (I-KF) has been developed to predict the attacker's future steps by estimating its (forward) KF's estimates. Existing works on inverse stochastic filtering and inverse cognition consider either linear Gaussian or finite state-space models, which limits their applicability in many practical engineering problems. Hence, in this dissertation, we study inverse filtering in the general non-linear non-Gaussian system framework and develop inverse non-linear filters using different sub-optimal approaches.
CommentsDoctoral thesis
