A resilient distributed state estimation algorithm based on invariant set-membership consensus fusion filtering (ISMF) is proposed to address the target tracking problem in mobile wireless sensor networks (MWSNs) subject to nonlinear target motion, time-varying communication topologies, and deception attacks. First, by exploiting the mapping between Lie groups and their associated Lie algebras, a novel estimation error is defined and linearized. Combined with the minimum-trace ellipsoid principle, an effective time-prediction scheme for updating the prior ellipsoid is developed. Second, a covariance intersection (CI)-based consensus fusion mechanism is introduced to accommodate time-varying network topologies, where an adaptive threshold is designed to filter unreliable neighbor information and construct a trustworthy fused ellipsoid. Third, local measurement correction is carried out via ellipsoid intersection, together with an adaptive correction gain to suppress abnormal measurements caused by deception attacks. It is theoretically guaranteed that the resulting posterior ellipsoid always encloses the true target state, even when a subset of mobile nodes is subjected to dual-channel deception attacks, while the ellipsoid volume is progressively reduced through iterative fusion. Simulation results demonstrate that the proposed algorithm can effectively recover accurate target tracking performance under both single-channel and dual-channel deception attack scenarios, exhibiting superior robustness and resilience.