How to Calibrate Your Adversary's Capabilities? Inverse Filtering for Counter-Autonomous Systems
How to Calibrate Your Adversary's Capabilities? Inverse Filtering for Counter-Autonomous Systems
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DOI：
10.1109/tsp.2019.2956676
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发表时间：
2019-05
影响因子：
5.4
通讯作者：
V. Krishnamurthy;M. Rangaswamy
中科院分区：
文献类型：
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作者：
V. Krishnamurthy;M. Rangaswamy
我们考虑涉及“我们”和“对手”的对抗性贝叶斯信号处理问题。对手在噪声中观察我们的状态;更新我们状态的后验分布，然后基于此后验选择行动。鉴于知识的“我们的”状态和对手的行动序列中观察到的噪音，我们考虑三个问题：（一）如何可以估计对手的后验分布？估计后验是一个涉及随机测量的逆滤波问题-我们在贝叶斯设置中制定和解决这个问题的几个版本。(ii)如何估计对手的观测可能性？这告诉我们对手的传感器有多精确。我们计算的最大似然估计对手的观测可能性，我们的对手的行动，对手的行动是在响应估计我们的状态的测量。(iii)我们如何选择状态来最小化对手观测似然估计的协方差？“我们的”状态可以被看作是一个探测信号，它会导致对手采取行动;因此选择最佳状态序列是一个输入设计问题。上述问题的动机是反自治系统的设计：给定一个复杂的自主对手的行动的测量，我们的反自治系统如何估计对手的基本信念，预测未来的行动，从而防范这些行动。
We consider an adversarial Bayesian signal processing problem involving “us” and an “adversary”. The adversary observes our state in noise; updates its posterior distribution of our state and then chooses an action based on this posterior. Given knowledge of “our” state and sequence of adversary's actions observed in noise, we consider three problems: (i) How can the adversary's posterior distribution be estimated? Estimating the posterior is an inverse filtering problem involving a random measure - we formulate and solve several versions of this problem in a Bayesian setting. (ii) How can the adversary's observation likelihood be estimated? This tells us how accurate the adversary's sensors are. We compute the maximum likelihood estimator for the adversary's observation likelihood given our measurements of the adversary's actions where the adversary's actions are in response to estimating our state. (iii) How can the state be chosen by us to minimize the covariance of the estimate of the adversary's observation likelihood? “Our” state can be viewed as a probe signal which causes the adversary to act; so choosing the optimal state sequence is an input design problem. The above questions are motivated by the design of counter-autonomous systems: given measurements of the actions of a sophisticated autonomous adversary, how can our counter-autonomous system estimate the underlying belief of the adversary, predict future actions and therefore guard against these actions.