Bounded-error target localization and tracking in presence of decoys using a fleet of UAVs

Abstract

Abstract This paper addresses the problem of searching and tracking of an a priori unknown number of targets spread over some geographical area using a fleet of UAVs. State perturbations and measurement noises are assumed to belong to bounded sets. In the monitored geographical area, some decoys may be erroneously considered as targets when observed under specific conditions. A robust bounded-error estimation approach is proposed to evaluate, at each time step, sets guaranteed to contain the actual state of already localized true targets and decoys. A set containing the states of targets still to be discovered is also evaluated. These sets are used to determine the control inputs of UAVs so as to minimize the estimation uncertainty at future time steps. Simulations involving several UAVs show that the proposed robust set-membership estimator is able to estimate the state of all actual targets and to efficiently identify and eliminate decoys.