Fisher Information Approach for Masking the Sensing Plan: Applications in Multifunction Radars

How to design a Markov Decision Process (MDP) based radar controller thatmakes small sacrifices in performance to mask its sensing plan from anadversary? The radar controller purposefully minimizes the Fisher informationof its emissions so that an adversary cannot identify the controller's modelparameters accurately. Unlike classical open loop statistical inference, wherethe Fisher information serves as a lower bound for the achievable covariance,this paper employs the Fisher information as a design constraint for a closedloop radar controller to mask its sensing plan. We analytically derive aclosed-form expression for the determinant of the Fisher Information Matrix(FIM) pertaining to the parameters of the MDP-based controller. Subsequently,we constrain the MDP with respect to the determinant of the FIM. Numericalresults show that the introduction of minor perturbations to the MDP'stransition kernel and the total operation cost can reduce the FisherInformation of the emissions. Consequently, this reduction amplifies thevariability in policy and transition kernel estimation errors, thwarting theadversary's accuracy in estimating the controller's sensing plan.