Smart target tracking using sensor scheduling and particle filter

This paper addresses the problem of tracking a ldquosmartrdquo target, wherein the issue of the observerpsilas concealment against the target should be taken into account, as a smart target is able to detect when it is under surveillance and react in a manner that makes future surveillance more difficult. This work proposes a sensor scheduling strategy (SSS), which balances the tracking performance and the concealing quality of the observer. This SSS uses an approach known as covariance control, to reduce the use of the active sensor whilst guaranteeing the estimation accuracy. A robust unscented particle filtering (UPF) method is utilized to deal with the nonlinear and non-Gaussian problem. Meanwhile, a Rao-Blackwellised technique is adopted to improve the estimation performance and reduce the computational burdens. Results based on experiments with synthetic data are reported.