  This paper considers the problem to plan mobile sensor networks for target
localization task in GPS-denied environments. Most researches on mobile sensor
networks assume that the states of the sensing agents are precisely known
during their missions, which is not feasible under the absence of external
infrastructures such as GPS. Thus, we propose a new algorithm to solve this
problem by: (i) estimating the states of the sensing agents in addition to the
target's through the combination of a particle filter (PF) and extended Kalman
filters (EKF) and (ii) involving the uncertainty of the states of the sensing
agents in planning the sensor networks based on the combined filters. This
approach does not require any additional internal/external sensors nor the
prior knowledge of the surrounding environments. We demonstrate the limitations
of prior works in GPS-denied environments and the improvements from the
proposed algorithm through Monte Carlo experiments.
