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In tracking applications, the target state (e.g, position, velocity) can be estimated by processing the measurements collected from all deployed sensors at a central node. The estimation performance significantly relies on the accuracy of the sensor positions/rotations when data fusion is conducted. Since in practice precise knowledge of this sensor information may not be available, in this paper two sequential Monte Carlo (SMC) approaches are proposed to jointly estimate the target state and resolve the sensor position uncertainty. The first one uses the particle filter combined with the Gibbs sampling method to deal with the general sensor registration problem. The second one uses the Rao-Blackwellised particle filter for a special case where the uncertainty of the sensor is a nearly constant measurement bias.