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Two novel distributed particle filters with Gaussian mixer approximation are proposed to localize and track multiple moving targets in a wireless sensor network. The distributed particle filters run on a set of uncorrelated sensor cliques that are dynamically organized based on moving target trajectories. These two algorithms differ in how the distributive computing is performed. In the first algorithm, partial results are updated at each sensor clique sequentially based on partial results forwarded from a neighboring clique and local observations. In the second algorithm, all individual cliques compute partial estimates based only on local observations in parallel, and forward their estimates to a fusion center to obtain final output. In order to conserve bandwidth and power, the local sufficient statistics (belief) is approximated by a low dimensional Gaussian mixture model (GMM) before propagating among sensor cliques. We further prove that the posterior distribution estimated by distributed particle filter convergence almost surely to the posterior distribution estimated from a centralized Bayesian formula. Moreover, a data-adaptive application layer communication protocol is proposed to facilitate sensor self-organization and collaboration. Simulation results show that the proposed DPF with GMM approximation algorithms provide robust localization and tracking performance at much reduced communication overhead.