Abstract-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 counterautonomous system estimate the underlying belief of the adversary, predict future actions and therefore guard against these actions.
In particular, REF derived the optimal inverse filter (which computes the posterior over the adversary's current belief given a state-sequence and actions).
How to Calibrate Your Adversary's Capabilities? Inverse Filtering for Counter-Autonomous Systems
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Engineering Computer Science Psychology